[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Viraj Jasani updated HBASE-23887:
-
Priority: Major (was: Minor)
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Assignee: Danil Lipovoy
>Priority: Major
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png,
> BlockCacheEvictionProcess.gif, BlockCacheEvictionProcess.gif, PR#1257.diff,
> cmp.png, evict_BC100_vs_BC23.png, eviction_100p.png, eviction_100p.png,
> eviction_100p.png, gc_100p.png, graph.png, image-2020-06-07-08-11-11-929.png,
> image-2020-06-07-08-19-00-922.png, image-2020-06-07-12-07-24-903.png,
> image-2020-06-07-12-07-30-307.png, image-2020-06-08-17-38-45-159.png,
> image-2020-06-08-17-38-52-579.png, image-2020-06-08-18-35-48-366.png,
> image-2020-06-14-20-51-11-905.png, image-2020-06-22-05-57-45-578.png,
> image-2020-09-23-09-48-59-714.png, image-2020-09-23-10-06-11-189.png,
> ratio.png, ratio2.png, read_requests_100pBC_vs_23pBC.png, requests_100p.png,
> requests_100p.png, requests_new2_100p.png, requests_new_100p.png, scan.png,
> scan_and_gets.png, scan_and_gets2.png, wave.png, ycsb_logs.zip
>
>
> Hi!
> I first time here, correct me please if something wrong.
> All latest information is here:
> [https://docs.google.com/document/d/1X8jVnK_3lp9ibpX6lnISf_He-6xrHZL0jQQ7hoTV0-g/edit?usp=sharing]
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> ---
> Some information below isn't actual
> ---
>
>
> Example:
> Imagine we have little cache, just can fit only 1 block and we are trying to
> read 3 blocks with offsets:
> 124
> 198
> 223
> Current way - we put the block 124, then put 198, evict 124, put 223, evict
> 198. A lot of work (5 actions).
> With the feature - last few digits evenly distributed from 0 to 99. When we
> divide by modulus we got:
> 124 -> 24
> 198 -> 98
> 223 -> 23
> It helps to sort them. Some part, for example below 50 (if we set
> *hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip
> others. It means we will not try to handle the block 198 and save CPU for
> other job. In the result - we put block 124, then put 223, evict 124 (3
> actions).
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
> Other parameters help to control when this logic will be enabled. It means it
> will work only while heavy reading going on.
> hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
> eviction process that start to avoid of putting data to BlockCache
> hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
> evicted each time that start to avoid of putting data to BlockCache
> By default: if 10 times (100 secunds) evicted more than 10 MB (each time)
> then we start to skip 50% of data blocks.
> When heavy evitions process end then new logic off and will put into
> BlockCache all blocks again.
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Viraj Jasani updated HBASE-23887:
-
Attachment: PR#1257.diff
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Assignee: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png,
> BlockCacheEvictionProcess.gif, BlockCacheEvictionProcess.gif, PR#1257.diff,
> cmp.png, evict_BC100_vs_BC23.png, eviction_100p.png, eviction_100p.png,
> eviction_100p.png, gc_100p.png, graph.png, image-2020-06-07-08-11-11-929.png,
> image-2020-06-07-08-19-00-922.png, image-2020-06-07-12-07-24-903.png,
> image-2020-06-07-12-07-30-307.png, image-2020-06-08-17-38-45-159.png,
> image-2020-06-08-17-38-52-579.png, image-2020-06-08-18-35-48-366.png,
> image-2020-06-14-20-51-11-905.png, image-2020-06-22-05-57-45-578.png,
> image-2020-09-23-09-48-59-714.png, image-2020-09-23-10-06-11-189.png,
> ratio.png, ratio2.png, read_requests_100pBC_vs_23pBC.png, requests_100p.png,
> requests_100p.png, requests_new2_100p.png, requests_new_100p.png, scan.png,
> scan_and_gets.png, scan_and_gets2.png, wave.png, ycsb_logs.zip
>
>
> Hi!
> I first time here, correct me please if something wrong.
> All latest information is here:
> [https://docs.google.com/document/d/1X8jVnK_3lp9ibpX6lnISf_He-6xrHZL0jQQ7hoTV0-g/edit?usp=sharing]
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> ---
> Some information below isn't actual
> ---
>
>
> Example:
> Imagine we have little cache, just can fit only 1 block and we are trying to
> read 3 blocks with offsets:
> 124
> 198
> 223
> Current way - we put the block 124, then put 198, evict 124, put 223, evict
> 198. A lot of work (5 actions).
> With the feature - last few digits evenly distributed from 0 to 99. When we
> divide by modulus we got:
> 124 -> 24
> 198 -> 98
> 223 -> 23
> It helps to sort them. Some part, for example below 50 (if we set
> *hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip
> others. It means we will not try to handle the block 198 and save CPU for
> other job. In the result - we put block 124, then put 223, evict 124 (3
> actions).
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
> Other parameters help to control when this logic will be enabled. It means it
> will work only while heavy reading going on.
> hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
> eviction process that start to avoid of putting data to BlockCache
> hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
> evicted each time that start to avoid of putting data to BlockCache
> By default: if 10 times (100 secunds) evicted more than 10 MB (each time)
> then we start to skip 50% of data blocks.
> When heavy evitions process end then new logic off and will put into
> BlockCache all blocks again.
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Attachment: ycsb_logs.zip
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Assignee: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png,
> BlockCacheEvictionProcess.gif, BlockCacheEvictionProcess.gif, cmp.png,
> evict_BC100_vs_BC23.png, eviction_100p.png, eviction_100p.png,
> eviction_100p.png, gc_100p.png, graph.png, image-2020-06-07-08-11-11-929.png,
> image-2020-06-07-08-19-00-922.png, image-2020-06-07-12-07-24-903.png,
> image-2020-06-07-12-07-30-307.png, image-2020-06-08-17-38-45-159.png,
> image-2020-06-08-17-38-52-579.png, image-2020-06-08-18-35-48-366.png,
> image-2020-06-14-20-51-11-905.png, image-2020-06-22-05-57-45-578.png,
> image-2020-09-23-09-48-59-714.png, image-2020-09-23-10-06-11-189.png,
> ratio.png, ratio2.png, read_requests_100pBC_vs_23pBC.png, requests_100p.png,
> requests_100p.png, requests_new2_100p.png, requests_new_100p.png, scan.png,
> scan_and_gets.png, scan_and_gets2.png, wave.png, ycsb_logs.zip
>
>
> Hi!
> I first time here, correct me please if something wrong.
> All latest information is here:
> [https://docs.google.com/document/d/1X8jVnK_3lp9ibpX6lnISf_He-6xrHZL0jQQ7hoTV0-g/edit?usp=sharing]
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> ---
> Some information below isn't actual
> ---
>
>
> Example:
> Imagine we have little cache, just can fit only 1 block and we are trying to
> read 3 blocks with offsets:
> 124
> 198
> 223
> Current way - we put the block 124, then put 198, evict 124, put 223, evict
> 198. A lot of work (5 actions).
> With the feature - last few digits evenly distributed from 0 to 99. When we
> divide by modulus we got:
> 124 -> 24
> 198 -> 98
> 223 -> 23
> It helps to sort them. Some part, for example below 50 (if we set
> *hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip
> others. It means we will not try to handle the block 198 and save CPU for
> other job. In the result - we put block 124, then put 223, evict 124 (3
> actions).
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
> Other parameters help to control when this logic will be enabled. It means it
> will work only while heavy reading going on.
> hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
> eviction process that start to avoid of putting data to BlockCache
> hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
> evicted each time that start to avoid of putting data to BlockCache
> By default: if 10 times (100 secunds) evicted more than 10 MB (each time)
> then we start to skip 50% of data blocks.
> When heavy evitions process end then new logic off and will put into
> BlockCache all blocks again.
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Attachment: image-2020-09-23-10-06-11-189.png
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Assignee: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png,
> BlockCacheEvictionProcess.gif, BlockCacheEvictionProcess.gif, cmp.png,
> evict_BC100_vs_BC23.png, eviction_100p.png, eviction_100p.png,
> eviction_100p.png, gc_100p.png, graph.png, image-2020-06-07-08-11-11-929.png,
> image-2020-06-07-08-19-00-922.png, image-2020-06-07-12-07-24-903.png,
> image-2020-06-07-12-07-30-307.png, image-2020-06-08-17-38-45-159.png,
> image-2020-06-08-17-38-52-579.png, image-2020-06-08-18-35-48-366.png,
> image-2020-06-14-20-51-11-905.png, image-2020-06-22-05-57-45-578.png,
> image-2020-09-23-09-48-59-714.png, image-2020-09-23-10-06-11-189.png,
> ratio.png, ratio2.png, read_requests_100pBC_vs_23pBC.png, requests_100p.png,
> requests_100p.png, requests_new2_100p.png, requests_new_100p.png, scan.png,
> scan_and_gets.png, scan_and_gets2.png, wave.png
>
>
> Hi!
> I first time here, correct me please if something wrong.
> All latest information is here:
> [https://docs.google.com/document/d/1X8jVnK_3lp9ibpX6lnISf_He-6xrHZL0jQQ7hoTV0-g/edit?usp=sharing]
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> ---
> Some information below isn't actual
> ---
>
>
> Example:
> Imagine we have little cache, just can fit only 1 block and we are trying to
> read 3 blocks with offsets:
> 124
> 198
> 223
> Current way - we put the block 124, then put 198, evict 124, put 223, evict
> 198. A lot of work (5 actions).
> With the feature - last few digits evenly distributed from 0 to 99. When we
> divide by modulus we got:
> 124 -> 24
> 198 -> 98
> 223 -> 23
> It helps to sort them. Some part, for example below 50 (if we set
> *hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip
> others. It means we will not try to handle the block 198 and save CPU for
> other job. In the result - we put block 124, then put 223, evict 124 (3
> actions).
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
> Other parameters help to control when this logic will be enabled. It means it
> will work only while heavy reading going on.
> hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
> eviction process that start to avoid of putting data to BlockCache
> hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
> evicted each time that start to avoid of putting data to BlockCache
> By default: if 10 times (100 secunds) evicted more than 10 MB (each time)
> then we start to skip 50% of data blocks.
> When heavy evitions process end then new logic off and will put into
> BlockCache all blocks again.
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Attachment: image-2020-09-23-09-48-59-714.png
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Assignee: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png,
> BlockCacheEvictionProcess.gif, BlockCacheEvictionProcess.gif, cmp.png,
> evict_BC100_vs_BC23.png, eviction_100p.png, eviction_100p.png,
> eviction_100p.png, gc_100p.png, graph.png, image-2020-06-07-08-11-11-929.png,
> image-2020-06-07-08-19-00-922.png, image-2020-06-07-12-07-24-903.png,
> image-2020-06-07-12-07-30-307.png, image-2020-06-08-17-38-45-159.png,
> image-2020-06-08-17-38-52-579.png, image-2020-06-08-18-35-48-366.png,
> image-2020-06-14-20-51-11-905.png, image-2020-06-22-05-57-45-578.png,
> image-2020-09-23-09-48-59-714.png, ratio.png, ratio2.png,
> read_requests_100pBC_vs_23pBC.png, requests_100p.png, requests_100p.png,
> requests_new2_100p.png, requests_new_100p.png, scan.png, scan_and_gets.png,
> scan_and_gets2.png, wave.png
>
>
> Hi!
> I first time here, correct me please if something wrong.
> All latest information is here:
> [https://docs.google.com/document/d/1X8jVnK_3lp9ibpX6lnISf_He-6xrHZL0jQQ7hoTV0-g/edit?usp=sharing]
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> ---
> Some information below isn't actual
> ---
>
>
> Example:
> Imagine we have little cache, just can fit only 1 block and we are trying to
> read 3 blocks with offsets:
> 124
> 198
> 223
> Current way - we put the block 124, then put 198, evict 124, put 223, evict
> 198. A lot of work (5 actions).
> With the feature - last few digits evenly distributed from 0 to 99. When we
> divide by modulus we got:
> 124 -> 24
> 198 -> 98
> 223 -> 23
> It helps to sort them. Some part, for example below 50 (if we set
> *hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip
> others. It means we will not try to handle the block 198 and save CPU for
> other job. In the result - we put block 124, then put 223, evict 124 (3
> actions).
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
> Other parameters help to control when this logic will be enabled. It means it
> will work only while heavy reading going on.
> hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
> eviction process that start to avoid of putting data to BlockCache
> hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
> evicted each time that start to avoid of putting data to BlockCache
> By default: if 10 times (100 secunds) evicted more than 10 MB (each time)
> then we start to skip 50% of data blocks.
> When heavy evitions process end then new logic off and will put into
> BlockCache all blocks again.
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Attachment: ratio2.png
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Assignee: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png,
> BlockCacheEvictionProcess.gif, cmp.png, evict_BC100_vs_BC23.png,
> eviction_100p.png, eviction_100p.png, eviction_100p.png, gc_100p.png,
> graph.png, image-2020-06-07-08-11-11-929.png,
> image-2020-06-07-08-19-00-922.png, image-2020-06-07-12-07-24-903.png,
> image-2020-06-07-12-07-30-307.png, image-2020-06-08-17-38-45-159.png,
> image-2020-06-08-17-38-52-579.png, image-2020-06-08-18-35-48-366.png,
> image-2020-06-14-20-51-11-905.png, ratio.png, ratio2.png,
> read_requests_100pBC_vs_23pBC.png, requests_100p.png, requests_100p.png,
> requests_new2_100p.png, requests_new_100p.png, scan.png, scan_and_gets.png,
> scan_and_gets2.png, wave.png
>
>
> Hi!
> I first time here, correct me please if something wrong.
> All latest information is here:
> [https://docs.google.com/document/d/1X8jVnK_3lp9ibpX6lnISf_He-6xrHZL0jQQ7hoTV0-g/edit?usp=sharing]
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> ---
> Some information below isn't actual
> ---
>
>
> Example:
> Imagine we have little cache, just can fit only 1 block and we are trying to
> read 3 blocks with offsets:
> 124
> 198
> 223
> Current way - we put the block 124, then put 198, evict 124, put 223, evict
> 198. A lot of work (5 actions).
> With the feature - last few digits evenly distributed from 0 to 99. When we
> divide by modulus we got:
> 124 -> 24
> 198 -> 98
> 223 -> 23
> It helps to sort them. Some part, for example below 50 (if we set
> *hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip
> others. It means we will not try to handle the block 198 and save CPU for
> other job. In the result - we put block 124, then put 223, evict 124 (3
> actions).
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
> Other parameters help to control when this logic will be enabled. It means it
> will work only while heavy reading going on.
> hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
> eviction process that start to avoid of putting data to BlockCache
> hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
> evicted each time that start to avoid of putting data to BlockCache
> By default: if 10 times (100 secunds) evicted more than 10 MB (each time)
> then we start to skip 50% of data blocks.
> When heavy evitions process end then new logic off and will put into
> BlockCache all blocks again.
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Attachment: image-2020-06-14-20-51-11-905.png
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Assignee: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png,
> BlockCacheEvictionProcess.gif, cmp.png, evict_BC100_vs_BC23.png,
> eviction_100p.png, eviction_100p.png, eviction_100p.png, gc_100p.png,
> graph.png, image-2020-06-07-08-11-11-929.png,
> image-2020-06-07-08-19-00-922.png, image-2020-06-07-12-07-24-903.png,
> image-2020-06-07-12-07-30-307.png, image-2020-06-08-17-38-45-159.png,
> image-2020-06-08-17-38-52-579.png, image-2020-06-08-18-35-48-366.png,
> image-2020-06-14-20-51-11-905.png, ratio.png,
> read_requests_100pBC_vs_23pBC.png, requests_100p.png, requests_100p.png,
> requests_new2_100p.png, requests_new_100p.png, scan.png, scan_and_gets.png,
> scan_and_gets2.png, wave.png
>
>
> Hi!
> I first time here, correct me please if something wrong.
> All latest information is here:
> [https://docs.google.com/document/d/1X8jVnK_3lp9ibpX6lnISf_He-6xrHZL0jQQ7hoTV0-g/edit?usp=sharing]
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> ---
> Some information below isn't actual
> ---
>
>
> Example:
> Imagine we have little cache, just can fit only 1 block and we are trying to
> read 3 blocks with offsets:
> 124
> 198
> 223
> Current way - we put the block 124, then put 198, evict 124, put 223, evict
> 198. A lot of work (5 actions).
> With the feature - last few digits evenly distributed from 0 to 99. When we
> divide by modulus we got:
> 124 -> 24
> 198 -> 98
> 223 -> 23
> It helps to sort them. Some part, for example below 50 (if we set
> *hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip
> others. It means we will not try to handle the block 198 and save CPU for
> other job. In the result - we put block 124, then put 223, evict 124 (3
> actions).
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
> Other parameters help to control when this logic will be enabled. It means it
> will work only while heavy reading going on.
> hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
> eviction process that start to avoid of putting data to BlockCache
> hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
> evicted each time that start to avoid of putting data to BlockCache
> By default: if 10 times (100 secunds) evicted more than 10 MB (each time)
> then we start to skip 50% of data blocks.
> When heavy evitions process end then new logic off and will put into
> BlockCache all blocks again.
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Attachment: scan_and_gets2.png
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Assignee: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png,
> BlockCacheEvictionProcess.gif, cmp.png, evict_BC100_vs_BC23.png,
> eviction_100p.png, eviction_100p.png, eviction_100p.png, gc_100p.png,
> graph.png, image-2020-06-07-08-11-11-929.png,
> image-2020-06-07-08-19-00-922.png, image-2020-06-07-12-07-24-903.png,
> image-2020-06-07-12-07-30-307.png, image-2020-06-08-17-38-45-159.png,
> image-2020-06-08-17-38-52-579.png, image-2020-06-08-18-35-48-366.png,
> ratio.png, read_requests_100pBC_vs_23pBC.png, requests_100p.png,
> requests_100p.png, requests_new2_100p.png, requests_new_100p.png, scan.png,
> scan_and_gets.png, scan_and_gets2.png, wave.png
>
>
> Hi!
> I first time here, correct me please if something wrong.
> All latest information is here:
> [https://docs.google.com/document/d/1X8jVnK_3lp9ibpX6lnISf_He-6xrHZL0jQQ7hoTV0-g/edit?usp=sharing]
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> ---
> Some information below isn't actual
> ---
>
>
> Example:
> Imagine we have little cache, just can fit only 1 block and we are trying to
> read 3 blocks with offsets:
> 124
> 198
> 223
> Current way - we put the block 124, then put 198, evict 124, put 223, evict
> 198. A lot of work (5 actions).
> With the feature - last few digits evenly distributed from 0 to 99. When we
> divide by modulus we got:
> 124 -> 24
> 198 -> 98
> 223 -> 23
> It helps to sort them. Some part, for example below 50 (if we set
> *hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip
> others. It means we will not try to handle the block 198 and save CPU for
> other job. In the result - we put block 124, then put 223, evict 124 (3
> actions).
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
> Other parameters help to control when this logic will be enabled. It means it
> will work only while heavy reading going on.
> hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
> eviction process that start to avoid of putting data to BlockCache
> hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
> evicted each time that start to avoid of putting data to BlockCache
> By default: if 10 times (100 secunds) evicted more than 10 MB (each time)
> then we start to skip 50% of data blocks.
> When heavy evitions process end then new logic off and will put into
> BlockCache all blocks again.
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Attachment: ratio.png
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Assignee: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png,
> BlockCacheEvictionProcess.gif, cmp.png, evict_BC100_vs_BC23.png,
> eviction_100p.png, eviction_100p.png, eviction_100p.png, gc_100p.png,
> graph.png, image-2020-06-07-08-11-11-929.png,
> image-2020-06-07-08-19-00-922.png, image-2020-06-07-12-07-24-903.png,
> image-2020-06-07-12-07-30-307.png, image-2020-06-08-17-38-45-159.png,
> image-2020-06-08-17-38-52-579.png, image-2020-06-08-18-35-48-366.png,
> ratio.png, read_requests_100pBC_vs_23pBC.png, requests_100p.png,
> requests_100p.png, requests_new2_100p.png, requests_new_100p.png, scan.png,
> scan_and_gets.png, wave.png
>
>
> Hi!
> I first time here, correct me please if something wrong.
> All latest information is here:
> [https://docs.google.com/document/d/1X8jVnK_3lp9ibpX6lnISf_He-6xrHZL0jQQ7hoTV0-g/edit?usp=sharing]
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> ---
> Some information below isn't actual
> ---
>
>
> Example:
> Imagine we have little cache, just can fit only 1 block and we are trying to
> read 3 blocks with offsets:
> 124
> 198
> 223
> Current way - we put the block 124, then put 198, evict 124, put 223, evict
> 198. A lot of work (5 actions).
> With the feature - last few digits evenly distributed from 0 to 99. When we
> divide by modulus we got:
> 124 -> 24
> 198 -> 98
> 223 -> 23
> It helps to sort them. Some part, for example below 50 (if we set
> *hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip
> others. It means we will not try to handle the block 198 and save CPU for
> other job. In the result - we put block 124, then put 223, evict 124 (3
> actions).
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
> Other parameters help to control when this logic will be enabled. It means it
> will work only while heavy reading going on.
> hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
> eviction process that start to avoid of putting data to BlockCache
> hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
> evicted each time that start to avoid of putting data to BlockCache
> By default: if 10 times (100 secunds) evicted more than 10 MB (each time)
> then we start to skip 50% of data blocks.
> When heavy evitions process end then new logic off and will put into
> BlockCache all blocks again.
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Attachment: scan_and_gets.png
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Assignee: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png,
> BlockCacheEvictionProcess.gif, cmp.png, evict_BC100_vs_BC23.png,
> eviction_100p.png, eviction_100p.png, eviction_100p.png, gc_100p.png,
> graph.png, image-2020-06-07-08-11-11-929.png,
> image-2020-06-07-08-19-00-922.png, image-2020-06-07-12-07-24-903.png,
> image-2020-06-07-12-07-30-307.png, image-2020-06-08-17-38-45-159.png,
> image-2020-06-08-17-38-52-579.png, image-2020-06-08-18-35-48-366.png,
> read_requests_100pBC_vs_23pBC.png, requests_100p.png, requests_100p.png,
> requests_new2_100p.png, requests_new_100p.png, scan.png, scan_and_gets.png,
> wave.png
>
>
> Hi!
> I first time here, correct me please if something wrong.
> All latest information is here:
> [https://docs.google.com/document/d/1X8jVnK_3lp9ibpX6lnISf_He-6xrHZL0jQQ7hoTV0-g/edit?usp=sharing]
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> ---
> Some information below isn't actual
> ---
>
>
> Example:
> Imagine we have little cache, just can fit only 1 block and we are trying to
> read 3 blocks with offsets:
> 124
> 198
> 223
> Current way - we put the block 124, then put 198, evict 124, put 223, evict
> 198. A lot of work (5 actions).
> With the feature - last few digits evenly distributed from 0 to 99. When we
> divide by modulus we got:
> 124 -> 24
> 198 -> 98
> 223 -> 23
> It helps to sort them. Some part, for example below 50 (if we set
> *hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip
> others. It means we will not try to handle the block 198 and save CPU for
> other job. In the result - we put block 124, then put 223, evict 124 (3
> actions).
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
> Other parameters help to control when this logic will be enabled. It means it
> will work only while heavy reading going on.
> hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
> eviction process that start to avoid of putting data to BlockCache
> hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
> evicted each time that start to avoid of putting data to BlockCache
> By default: if 10 times (100 secunds) evicted more than 10 MB (each time)
> then we start to skip 50% of data blocks.
> When heavy evitions process end then new logic off and will put into
> BlockCache all blocks again.
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
--
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[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Description:
Hi!
I first time here, correct me please if something wrong.
All latest information is here:
[https://docs.google.com/document/d/1X8jVnK_3lp9ibpX6lnISf_He-6xrHZL0jQQ7hoTV0-g/edit?usp=sharing]
I want propose how to improve performance when data in HFiles much more than
BlockChache (usual story in BigData). The idea - caching only part of DATA
blocks. It is good becouse LruBlockCache starts to work and save huge amount of
GC.
Sometimes we have more data than can fit into BlockCache and it is cause a high
rate of evictions. In this case we can skip cache a block N and insted cache
the N+1th block. Anyway we would evict N block quite soon and that why that
skipping good for performance.
---
Some information below isn't actual
---
Example:
Imagine we have little cache, just can fit only 1 block and we are trying to
read 3 blocks with offsets:
124
198
223
Current way - we put the block 124, then put 198, evict 124, put 223, evict
198. A lot of work (5 actions).
With the feature - last few digits evenly distributed from 0 to 99. When we
divide by modulus we got:
124 -> 24
198 -> 98
223 -> 23
It helps to sort them. Some part, for example below 50 (if we set
*hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip others.
It means we will not try to handle the block 198 and save CPU for other job. In
the result - we put block 124, then put 223, evict 124 (3 actions).
See the picture in attachment with test below. Requests per second is higher,
GC is lower.
The key point of the code:
Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
100
But if we set it 1-99, then will work the next logic:
{code:java}
public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean inMemory)
{
if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
return;
...
// the same code as usual
}
{code}
Other parameters help to control when this logic will be enabled. It means it
will work only while heavy reading going on.
hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
eviction process that start to avoid of putting data to BlockCache
hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
evicted each time that start to avoid of putting data to BlockCache
By default: if 10 times (100 secunds) evicted more than 10 MB (each time) then
we start to skip 50% of data blocks.
When heavy evitions process end then new logic off and will put into
BlockCache all blocks again.
Descriptions of the test:
4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
4 RegionServers
4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
Total BlockCache Size = 48 Gb (8 % of data in HFiles)
Random read in 20 threads
I am going to make Pull Request, hope it is right way to make some contribution
in this cool product.
was:
Hi!
I first time here, correct me please if something wrong.
I want propose how to improve performance when data in HFiles much more than
BlockChache (usual story in BigData). The idea - caching only part of DATA
blocks. It is good becouse LruBlockCache starts to work and save huge amount of
GC.
Sometimes we have more data than can fit into BlockCache and it is cause a high
rate of evictions. In this case we can skip cache a block N and insted cache
the N+1th block. Anyway we would evict N block quite soon and that why that
skipping good for performance.
Example:
Imagine we have little cache, just can fit only 1 block and we are trying to
read 3 blocks with offsets:
124
198
223
Current way - we put the block 124, then put 198, evict 124, put 223, evict
198. A lot of work (5 actions).
With the feature - last few digits evenly distributed from 0 to 99. When we
divide by modulus we got:
124 -> 24
198 -> 98
223 -> 23
It helps to sort them. Some part, for example below 50 (if we set
*hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip others.
It means we will not try to handle the block 198 and save CPU for other job. In
the result - we put block 124, then put 223, evict 124 (3 actions).
See the picture in attachment with test below. Requests per second is higher,
GC is lower.
The key point of the code:
Added the parameter: *hbase.lru.cache.data.block.percent* which by default = 100
But if we set it 1-99, then will work the next logic:
{code:java}
public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean inMemory)
{
if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
return;
...
// the same
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Attachment: image-2020-06-07-12-07-30-307.png
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png,
> BlockCacheEvictionProcess.gif, cmp.png, evict_BC100_vs_BC23.png,
> eviction_100p.png, eviction_100p.png, eviction_100p.png, gc_100p.png,
> graph.png, image-2020-06-07-08-11-11-929.png,
> image-2020-06-07-08-19-00-922.png, image-2020-06-07-12-07-24-903.png,
> image-2020-06-07-12-07-30-307.png, read_requests_100pBC_vs_23pBC.png,
> requests_100p.png, requests_100p.png, requests_new2_100p.png,
> requests_new_100p.png, scan.png, wave.png
>
>
> Hi!
> I first time here, correct me please if something wrong.
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> Example:
> Imagine we have little cache, just can fit only 1 block and we are trying to
> read 3 blocks with offsets:
> 124
> 198
> 223
> Current way - we put the block 124, then put 198, evict 124, put 223, evict
> 198. A lot of work (5 actions).
> With the feature - last few digits evenly distributed from 0 to 99. When we
> divide by modulus we got:
> 124 -> 24
> 198 -> 98
> 223 -> 23
> It helps to sort them. Some part, for example below 50 (if we set
> *hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip
> others. It means we will not try to handle the block 198 and save CPU for
> other job. In the result - we put block 124, then put 223, evict 124 (3
> actions).
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
> Other parameters help to control when this logic will be enabled. It means it
> will work only while heavy reading going on.
> hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
> eviction process that start to avoid of putting data to BlockCache
> hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
> evicted each time that start to avoid of putting data to BlockCache
> By default: if 10 times (100 secunds) evicted more than 10 MB (each time)
> then we start to skip 50% of data blocks.
> When heavy evitions process end then new logic off and will put into
> BlockCache all blocks again.
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Attachment: image-2020-06-07-12-07-24-903.png
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png,
> BlockCacheEvictionProcess.gif, cmp.png, evict_BC100_vs_BC23.png,
> eviction_100p.png, eviction_100p.png, eviction_100p.png, gc_100p.png,
> graph.png, image-2020-06-07-08-11-11-929.png,
> image-2020-06-07-08-19-00-922.png, image-2020-06-07-12-07-24-903.png,
> image-2020-06-07-12-07-30-307.png, read_requests_100pBC_vs_23pBC.png,
> requests_100p.png, requests_100p.png, requests_new2_100p.png,
> requests_new_100p.png, scan.png, wave.png
>
>
> Hi!
> I first time here, correct me please if something wrong.
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> Example:
> Imagine we have little cache, just can fit only 1 block and we are trying to
> read 3 blocks with offsets:
> 124
> 198
> 223
> Current way - we put the block 124, then put 198, evict 124, put 223, evict
> 198. A lot of work (5 actions).
> With the feature - last few digits evenly distributed from 0 to 99. When we
> divide by modulus we got:
> 124 -> 24
> 198 -> 98
> 223 -> 23
> It helps to sort them. Some part, for example below 50 (if we set
> *hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip
> others. It means we will not try to handle the block 198 and save CPU for
> other job. In the result - we put block 124, then put 223, evict 124 (3
> actions).
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
> Other parameters help to control when this logic will be enabled. It means it
> will work only while heavy reading going on.
> hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
> eviction process that start to avoid of putting data to BlockCache
> hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
> evicted each time that start to avoid of putting data to BlockCache
> By default: if 10 times (100 secunds) evicted more than 10 MB (each time)
> then we start to skip 50% of data blocks.
> When heavy evitions process end then new logic off and will put into
> BlockCache all blocks again.
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Attachment: wave.png
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png,
> BlockCacheEvictionProcess.gif, cmp.png, evict_BC100_vs_BC23.png,
> eviction_100p.png, eviction_100p.png, eviction_100p.png, gc_100p.png,
> graph.png, image-2020-06-07-08-11-11-929.png,
> image-2020-06-07-08-19-00-922.png, read_requests_100pBC_vs_23pBC.png,
> requests_100p.png, requests_100p.png, requests_new2_100p.png,
> requests_new_100p.png, scan.png, wave.png
>
>
> Hi!
> I first time here, correct me please if something wrong.
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> Example:
> Imagine we have little cache, just can fit only 1 block and we are trying to
> read 3 blocks with offsets:
> 124
> 198
> 223
> Current way - we put the block 124, then put 198, evict 124, put 223, evict
> 198. A lot of work (5 actions).
> With the feature - last few digits evenly distributed from 0 to 99. When we
> divide by modulus we got:
> 124 -> 24
> 198 -> 98
> 223 -> 23
> It helps to sort them. Some part, for example below 50 (if we set
> *hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip
> others. It means we will not try to handle the block 198 and save CPU for
> other job. In the result - we put block 124, then put 223, evict 124 (3
> actions).
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
> Other parameters help to control when this logic will be enabled. It means it
> will work only while heavy reading going on.
> hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
> eviction process that start to avoid of putting data to BlockCache
> hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
> evicted each time that start to avoid of putting data to BlockCache
> By default: if 10 times (100 secunds) evicted more than 10 MB (each time)
> then we start to skip 50% of data blocks.
> When heavy evitions process end then new logic off and will put into
> BlockCache all blocks again.
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Attachment: requests_new2_100p.png
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png,
> BlockCacheEvictionProcess.gif, cmp.png, evict_BC100_vs_BC23.png,
> eviction_100p.png, eviction_100p.png, eviction_100p.png, gc_100p.png,
> graph.png, read_requests_100pBC_vs_23pBC.png, requests_100p.png,
> requests_100p.png, requests_new2_100p.png, requests_new_100p.png, scan.png
>
>
> Hi!
> I first time here, correct me please if something wrong.
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> Example:
> Imagine we have little cache, just can fit only 1 block and we are trying to
> read 3 blocks with offsets:
> 124
> 198
> 223
> Current way - we put the block 124, then put 198, evict 124, put 223, evict
> 198. A lot of work (5 actions).
> With the feature - last few digits evenly distributed from 0 to 99. When we
> divide by modulus we got:
> 124 -> 24
> 198 -> 98
> 223 -> 23
> It helps to sort them. Some part, for example below 50 (if we set
> *hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip
> others. It means we will not try to handle the block 198 and save CPU for
> other job. In the result - we put block 124, then put 223, evict 124 (3
> actions).
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
> Other parameters help to control when this logic will be enabled. It means it
> will work only while heavy reading going on.
> hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
> eviction process that start to avoid of putting data to BlockCache
> hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
> evicted each time that start to avoid of putting data to BlockCache
> By default: if 10 times (100 secunds) evicted more than 10 MB (each time)
> then we start to skip 50% of data blocks.
> When heavy evitions process end then new logic off and will put into
> BlockCache all blocks again.
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Attachment: graph.png
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png,
> BlockCacheEvictionProcess.gif, cmp.png, evict_BC100_vs_BC23.png,
> eviction_100p.png, eviction_100p.png, eviction_100p.png, gc_100p.png,
> graph.png, read_requests_100pBC_vs_23pBC.png, requests_100p.png,
> requests_100p.png, requests_new_100p.png, scan.png
>
>
> Hi!
> I first time here, correct me please if something wrong.
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> Example:
> Imagine we have little cache, just can fit only 1 block and we are trying to
> read 3 blocks with offsets:
> 124
> 198
> 223
> Current way - we put the block 124, then put 198, evict 124, put 223, evict
> 198. A lot of work (5 actions).
> With the feature - last few digits evenly distributed from 0 to 99. When we
> divide by modulus we got:
> 124 -> 24
> 198 -> 98
> 223 -> 23
> It helps to sort them. Some part, for example below 50 (if we set
> *hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip
> others. It means we will not try to handle the block 198 and save CPU for
> other job. In the result - we put block 124, then put 223, evict 124 (3
> actions).
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
> Other parameters help to control when this logic will be enabled. It means it
> will work only while heavy reading going on.
> hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
> eviction process that start to avoid of putting data to BlockCache
> hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
> evicted each time that start to avoid of putting data to BlockCache
> By default: if 10 times (100 secunds) evicted more than 10 MB (each time)
> then we start to skip 50% of data blocks.
> When heavy evitions process end then new logic off and will put into
> BlockCache all blocks again.
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
--
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[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Attachment: scan.png
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png,
> BlockCacheEvictionProcess.gif, cmp.png, evict_BC100_vs_BC23.png,
> eviction_100p.png, eviction_100p.png, eviction_100p.png, gc_100p.png,
> read_requests_100pBC_vs_23pBC.png, requests_100p.png, requests_100p.png,
> requests_new_100p.png, scan.png
>
>
> Hi!
> I first time here, correct me please if something wrong.
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> Example:
> Imagine we have little cache, just can fit only 1 block and we are trying to
> read 3 blocks with offsets:
> 124
> 198
> 223
> Current way - we put the block 124, then put 198, evict 124, put 223, evict
> 198. A lot of work (5 actions).
> With the feature - last few digits evenly distributed from 0 to 99. When we
> divide by modulus we got:
> 124 -> 24
> 198 -> 98
> 223 -> 23
> It helps to sort them. Some part, for example below 50 (if we set
> *hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip
> others. It means we will not try to handle the block 198 and save CPU for
> other job. In the result - we put block 124, then put 223, evict 124 (3
> actions).
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
> Other parameters help to control when this logic will be enabled. It means it
> will work only while heavy reading going on.
> hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
> eviction process that start to avoid of putting data to BlockCache
> hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
> evicted each time that start to avoid of putting data to BlockCache
> By default: if 10 times (100 secunds) evicted more than 10 MB (each time)
> then we start to skip 50% of data blocks.
> When heavy evitions process end then new logic off and will put into
> BlockCache all blocks again.
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
--
This message was sent by Atlassian Jira
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[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Attachment: requests_new_100p.png
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png,
> BlockCacheEvictionProcess.gif, cmp.png, evict_BC100_vs_BC23.png,
> eviction_100p.png, eviction_100p.png, eviction_100p.png, gc_100p.png,
> read_requests_100pBC_vs_23pBC.png, requests_100p.png, requests_100p.png,
> requests_new_100p.png
>
>
> Hi!
> I first time here, correct me please if something wrong.
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> Example:
> Imagine we have little cache, just can fit only 1 block and we are trying to
> read 3 blocks with offsets:
> 124
> 198
> 223
> Current way - we put the block 124, then put 198, evict 124, put 223, evict
> 198. A lot of work (5 actions).
> With the feature - last few digits evenly distributed from 0 to 99. When we
> divide by modulus we got:
> 124 -> 24
> 198 -> 98
> 223 -> 23
> It helps to sort them. Some part, for example below 50 (if we set
> *hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip
> others. It means we will not try to handle the block 198 and save CPU for
> other job. In the result - we put block 124, then put 223, evict 124 (3
> actions).
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
> Other parameters help to control when this logic will be enabled. It means it
> will work only while heavy reading going on.
> hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
> eviction process that start to avoid of putting data to BlockCache
> hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
> evicted each time that start to avoid of putting data to BlockCache
> By default: if 10 times (100 secunds) evicted more than 10 MB (each time)
> then we start to skip 50% of data blocks.
> When heavy evitions process end then new logic off and will put into
> BlockCache all blocks again.
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Attachment: requests_100p.png
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png,
> BlockCacheEvictionProcess.gif, cmp.png, evict_BC100_vs_BC23.png,
> eviction_100p.png, eviction_100p.png, eviction_100p.png, gc_100p.png,
> read_requests_100pBC_vs_23pBC.png, requests_100p.png, requests_100p.png
>
>
> Hi!
> I first time here, correct me please if something wrong.
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> Example:
> Imagine we have little cache, just can fit only 1 block and we are trying to
> read 3 blocks with offsets:
> 124
> 198
> 223
> Current way - we put the block 124, then put 198, evict 124, put 223, evict
> 198. A lot of work (5 actions).
> With the feature - last few digits evenly distributed from 0 to 99. When we
> divide by modulus we got:
> 124 -> 24
> 198 -> 98
> 223 -> 23
> It helps to sort them. Some part, for example below 50 (if we set
> *hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip
> others. It means we will not try to handle the block 198 and save CPU for
> other job. In the result - we put block 124, then put 223, evict 124 (3
> actions).
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
> Other parameters help to control when this logic will be enabled. It means it
> will work only while heavy reading going on.
> hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
> eviction process that start to avoid of putting data to BlockCache
> hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
> evicted each time that start to avoid of putting data to BlockCache
> By default: if 10 times (100 secunds) evicted more than 10 MB (each time)
> then we start to skip 50% of data blocks.
> When heavy evitions process end then new logic off and will put into
> BlockCache all blocks again.
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Attachment: eviction_100p.png
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png,
> BlockCacheEvictionProcess.gif, cmp.png, evict_BC100_vs_BC23.png,
> eviction_100p.png, eviction_100p.png, eviction_100p.png, gc_100p.png,
> read_requests_100pBC_vs_23pBC.png, requests_100p.png
>
>
> Hi!
> I first time here, correct me please if something wrong.
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> Example:
> Imagine we have little cache, just can fit only 1 block and we are trying to
> read 3 blocks with offsets:
> 124
> 198
> 223
> Current way - we put the block 124, then put 198, evict 124, put 223, evict
> 198. A lot of work (5 actions).
> With the feature - last few digits evenly distributed from 0 to 99. When we
> divide by modulus we got:
> 124 -> 24
> 198 -> 98
> 223 -> 23
> It helps to sort them. Some part, for example below 50 (if we set
> *hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip
> others. It means we will not try to handle the block 198 and save CPU for
> other job. In the result - we put block 124, then put 223, evict 124 (3
> actions).
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
> Other parameters help to control when this logic will be enabled. It means it
> will work only while heavy reading going on.
> hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
> eviction process that start to avoid of putting data to BlockCache
> hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
> evicted each time that start to avoid of putting data to BlockCache
> By default: if 10 times (100 secunds) evicted more than 10 MB (each time)
> then we start to skip 50% of data blocks.
> When heavy evitions process end then new logic off and will put into
> BlockCache all blocks again.
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Attachment: gc_100p.png
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png,
> BlockCacheEvictionProcess.gif, cmp.png, evict_BC100_vs_BC23.png,
> eviction_100p.png, eviction_100p.png, gc_100p.png,
> read_requests_100pBC_vs_23pBC.png, requests_100p.png
>
>
> Hi!
> I first time here, correct me please if something wrong.
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> Example:
> Imagine we have little cache, just can fit only 1 block and we are trying to
> read 3 blocks with offsets:
> 124
> 198
> 223
> Current way - we put the block 124, then put 198, evict 124, put 223, evict
> 198. A lot of work (5 actions).
> With the feature - last few digits evenly distributed from 0 to 99. When we
> divide by modulus we got:
> 124 -> 24
> 198 -> 98
> 223 -> 23
> It helps to sort them. Some part, for example below 50 (if we set
> *hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip
> others. It means we will not try to handle the block 198 and save CPU for
> other job. In the result - we put block 124, then put 223, evict 124 (3
> actions).
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
> Other parameters help to control when this logic will be enabled. It means it
> will work only while heavy reading going on.
> hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
> eviction process that start to avoid of putting data to BlockCache
> hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
> evicted each time that start to avoid of putting data to BlockCache
> By default: if 10 times (100 secunds) evicted more than 10 MB (each time)
> then we start to skip 50% of data blocks.
> When heavy evitions process end then new logic off and will put into
> BlockCache all blocks again.
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Attachment: eviction_100p.png
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png,
> BlockCacheEvictionProcess.gif, cmp.png, evict_BC100_vs_BC23.png,
> eviction_100p.png, eviction_100p.png, read_requests_100pBC_vs_23pBC.png,
> requests_100p.png
>
>
> Hi!
> I first time here, correct me please if something wrong.
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> Example:
> Imagine we have little cache, just can fit only 1 block and we are trying to
> read 3 blocks with offsets:
> 124
> 198
> 223
> Current way - we put the block 124, then put 198, evict 124, put 223, evict
> 198. A lot of work (5 actions).
> With the feature - last few digits evenly distributed from 0 to 99. When we
> divide by modulus we got:
> 124 -> 24
> 198 -> 98
> 223 -> 23
> It helps to sort them. Some part, for example below 50 (if we set
> *hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip
> others. It means we will not try to handle the block 198 and save CPU for
> other job. In the result - we put block 124, then put 223, evict 124 (3
> actions).
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
> Other parameters help to control when this logic will be enabled. It means it
> will work only while heavy reading going on.
> hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
> eviction process that start to avoid of putting data to BlockCache
> hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
> evicted each time that start to avoid of putting data to BlockCache
> By default: if 10 times (100 secunds) evicted more than 10 MB (each time)
> then we start to skip 50% of data blocks.
> When heavy evitions process end then new logic off and will put into
> BlockCache all blocks again.
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Attachment: eviction_100p.png
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png,
> BlockCacheEvictionProcess.gif, cmp.png, evict_BC100_vs_BC23.png,
> eviction_100p.png, read_requests_100pBC_vs_23pBC.png, requests_100p.png
>
>
> Hi!
> I first time here, correct me please if something wrong.
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> Example:
> Imagine we have little cache, just can fit only 1 block and we are trying to
> read 3 blocks with offsets:
> 124
> 198
> 223
> Current way - we put the block 124, then put 198, evict 124, put 223, evict
> 198. A lot of work (5 actions).
> With the feature - last few digits evenly distributed from 0 to 99. When we
> divide by modulus we got:
> 124 -> 24
> 198 -> 98
> 223 -> 23
> It helps to sort them. Some part, for example below 50 (if we set
> *hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip
> others. It means we will not try to handle the block 198 and save CPU for
> other job. In the result - we put block 124, then put 223, evict 124 (3
> actions).
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
> Other parameters help to control when this logic will be enabled. It means it
> will work only while heavy reading going on.
> hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
> eviction process that start to avoid of putting data to BlockCache
> hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
> evicted each time that start to avoid of putting data to BlockCache
> By default: if 10 times (100 secunds) evicted more than 10 MB (each time)
> then we start to skip 50% of data blocks.
> When heavy evitions process end then new logic off and will put into
> BlockCache all blocks again.
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
--
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[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Attachment: requests_100p.png
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png,
> BlockCacheEvictionProcess.gif, cmp.png, evict_BC100_vs_BC23.png,
> read_requests_100pBC_vs_23pBC.png, requests_100p.png
>
>
> Hi!
> I first time here, correct me please if something wrong.
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> Example:
> Imagine we have little cache, just can fit only 1 block and we are trying to
> read 3 blocks with offsets:
> 124
> 198
> 223
> Current way - we put the block 124, then put 198, evict 124, put 223, evict
> 198. A lot of work (5 actions).
> With the feature - last few digits evenly distributed from 0 to 99. When we
> divide by modulus we got:
> 124 -> 24
> 198 -> 98
> 223 -> 23
> It helps to sort them. Some part, for example below 50 (if we set
> *hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip
> others. It means we will not try to handle the block 198 and save CPU for
> other job. In the result - we put block 124, then put 223, evict 124 (3
> actions).
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
> Other parameters help to control when this logic will be enabled. It means it
> will work only while heavy reading going on.
> hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
> eviction process that start to avoid of putting data to BlockCache
> hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
> evicted each time that start to avoid of putting data to BlockCache
> By default: if 10 times (100 secunds) evicted more than 10 MB (each time)
> then we start to skip 50% of data blocks.
> When heavy evitions process end then new logic off and will put into
> BlockCache all blocks again.
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Attachment: BlockCacheEvictionProcess.gif
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png,
> BlockCacheEvictionProcess.gif, cmp.png, evict_BC100_vs_BC23.png,
> read_requests_100pBC_vs_23pBC.png
>
>
> Hi!
> I first time here, correct me please if something wrong.
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> Example:
> Imagine we have little cache, just can fit only 1 block and we are trying to
> read 3 blocks with offsets:
> 124
> 198
> 223
> Current way - we put the block 124, then put 198, evict 124, put 223, evict
> 198. A lot of work (5 actions).
> With the feature - last few digits evenly distributed from 0 to 99. When we
> divide by modulus we got:
> 124 -> 24
> 198 -> 98
> 223 -> 23
> It helps to sort them. Some part, for example below 50 (if we set
> *hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip
> others. It means we will not try to handle the block 198 and save CPU for
> other job. In the result - we put block 124, then put 223, evict 124 (3
> actions).
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
> Other parameters help to control when this logic will be enabled. It means it
> will work only while heavy reading going on.
> hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
> eviction process that start to avoid of putting data to BlockCache
> hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
> evicted each time that start to avoid of putting data to BlockCache
> By default: if 10 times (100 secunds) evicted more than 10 MB (each time)
> then we start to skip 50% of data blocks.
> When heavy evitions process end then new logic off and will put into
> BlockCache all blocks again.
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Description:
Hi!
I first time here, correct me please if something wrong.
I want propose how to improve performance when data in HFiles much more than
BlockChache (usual story in BigData). The idea - caching only part of DATA
blocks. It is good becouse LruBlockCache starts to work and save huge amount of
GC.
Sometimes we have more data than can fit into BlockCache and it is cause a high
rate of evictions. In this case we can skip cache a block N and insted cache
the N+1th block. Anyway we would evict N block quite soon and that why that
skipping good for performance.
Example:
Imagine we have little cache, just can fit only 1 block and we are trying to
read 3 blocks with offsets:
124
198
223
Current way - we put the block 124, then put 198, evict 124, put 223, evict
198. A lot of work (5 actions).
With the feature - last few digits evenly distributed from 0 to 99. When we
divide by modulus we got:
124 -> 24
198 -> 98
223 -> 23
It helps to sort them. Some part, for example below 50 (if we set
*hbase.lru.cache.data.block.percent* = 50) go into the cache. And skip others.
It means we will not try to handle the block 198 and save CPU for other job. In
the result - we put block 124, then put 223, evict 124 (3 actions).
See the picture in attachment with test below. Requests per second is higher,
GC is lower.
The key point of the code:
Added the parameter: *hbase.lru.cache.data.block.percent* which by default = 100
But if we set it 1-99, then will work the next logic:
{code:java}
public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean inMemory)
{
if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
return;
...
// the same code as usual
}
{code}
Other parameters help to control when this logic will be enabled. It means it
will work only while heavy reading going on.
hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
eviction process that start to avoid of putting data to BlockCache
hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
evicted each time that start to avoid of putting data to BlockCache
By default: if 10 times (100 secunds) evicted more than 10 MB (each time) then
we start to skip 50% of data blocks.
When heavy evitions process end then new logic off and will put into BlockCache
all blocks again.
Descriptions of the test:
4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
4 RegionServers
4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
Total BlockCache Size = 48 Gb (8 % of data in HFiles)
Random read in 20 threads
I am going to make Pull Request, hope it is right way to make some contribution
in this cool product.
was:
Hi!
I first time here, correct me please if something wrong.
I want propose how to improve performance when data in HFiles much more than
BlockChache (usual story in BigData). The idea - caching only part of DATA
blocks. It is good becouse LruBlockCache starts to work and save huge amount of
GC.
Sometimes we have more data than can fit into BlockCache and it is cause a high
rate of evictions. In this case we can skip cache a block N and insted cache
the N+1th block. Anyway we would evict N block quite soon and that why that
skipping good for performance.
See the picture in attachment with test below. Requests per second is higher,
GC is lower.
The key point of the code:
Added the parameter: *hbase.lru.cache.data.block.percent* which by default = 100
But if we set it 1-99, then will work the next logic:
{code:java}
public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean inMemory)
{
if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
return;
...
// the same code as usual
}
{code}
Other parameters help to control when this logic will be enabled.
hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
eviction process that start to avoid of putting data to BlockCache
hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
evicted each time that start to avoid of putting data to BlockCache
By default: if 10 times (100 secunds) evicted more than 10 MB (each time) then
we start to skip 50% of data blocks.
When heavy evitions process end then it will put into BlockCache all blocks
again.
Descriptions of the test:
4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
4 RegionServers
4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
Total BlockCache Size = 48 Gb (8 % of data in HFiles)
Random read in 20 threads
I am going to make Pull Request,
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Description:
Hi!
I first time here, correct me please if something wrong.
I want propose how to improve performance when data in HFiles much more than
BlockChache (usual story in BigData). The idea - caching only part of DATA
blocks. It is good becouse LruBlockCache starts to work and save huge amount of
GC.
Sometimes we have more data than can fit into BlockCache and it is cause a high
rate of evictions. In this case we can skip cache a block N and insted cache
the N+1th block. Anyway we would evict N block quite soon and that why that
skipping good for performance.
See the picture in attachment with test below. Requests per second is higher,
GC is lower.
The key point of the code:
Added the parameter: *hbase.lru.cache.data.block.percent* which by default = 100
But if we set it 1-99, then will work the next logic:
{code:java}
public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean inMemory)
{
if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
return;
...
// the same code as usual
}
{code}
Other parameters help to control when this logic will be enabled.
hbase.lru.cache.heavy.eviction.count.limit - set how many times have to run
eviction process that start to avoid of putting data to BlockCache
hbase.lru.cache.heavy.eviction.bytes.size.limit - set how many bytes have to
evicted each time that start to avoid of putting data to BlockCache
By default: if 10 times (100 secunds) evicted more than 10 MB (each time) then
we start to skip 50% of data blocks.
When heavy evitions process end then it will put into BlockCache all blocks
again.
Descriptions of the test:
4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
4 RegionServers
4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
Total BlockCache Size = 48 Gb (8 % of data in HFiles)
Random read in 20 threads
I am going to make Pull Request, hope it is right way to make some contribution
in this cool product.
was:
Hi!
I first time here, correct me please if something wrong.
I want propose how to improve performance when data in HFiles much more than
BlockChache (usual story in BigData). The idea - caching only part of DATA
blocks. It is good becouse LruBlockCache starts to work and save huge amount of
GC.
Sometimes we have more data than can fit into BlockCache and it is cause a high
rate of evictions. In this case we can skip cache a block N and insted cache
the N+1th block. Anyway we would evict N block quite soon and that why that
skipping good for performance.
See the picture in attachment with test below. Requests per second is higher,
GC is lower.
The key point of the code:
Added the parameter: *hbase.lru.cache.data.block.percent* which by default = 100
But if we set it 1-99, then will work the next logic:
{code:java}
public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean inMemory)
{
if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
return;
...
// the same code as usual
}
{code}
Descriptions of the test:
4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
4 RegionServers
4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
Total BlockCache Size = 48 Gb (8 % of data in HFiles)
Random read in 20 threads
I am going to make Pull Request, hope it is right way to make some contribution
in this cool product.
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png, cmp.png,
> evict_BC100_vs_BC23.png, read_requests_100pBC_vs_23pBC.png
>
>
> Hi!
> I first time here, correct me please if something wrong.
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> See the picture in attachment with test below. Requests
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Description:
Hi!
I first time here, correct me please if something wrong.
I want propose how to improve performance when data in HFiles much more than
BlockChache (usual story in BigData). The idea - caching only part of DATA
blocks. It is good becouse LruBlockCache starts to work and save huge amount of
GC.
Sometimes we have more data than can fit into BlockCache and it is cause a high
rate of evictions. In this case we can skip cache a block N and insted cache
the N+1th block. Anyway we would evict N block quite soon and that why that
skipping good for performance.
See the picture in attachment with test below. Requests per second is higher,
GC is lower.
The key point of the code:
Added the parameter: *hbase.lru.cache.data.block.percent* which by default = 100
But if we set it 1-99, then will work the next logic:
{code:java}
public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean inMemory)
{
if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
return;
...
// the same code as usual
}
{code}
Descriptions of the test:
4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
4 RegionServers
4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
Total BlockCache Size = 48 Gb (8 % of data in HFiles)
Random read in 20 threads
I am going to make Pull Request, hope it is right way to make some contribution
in this cool product.
was:
Hi!
I first time here, correct me please if something wrong.
I want propose how to improve performance when data in HFiles much more than
BlockChache (usual story in BigData). The idea - caching only part of DATA
blocks. It is good becouse LruBlockCache starts to work and save huge amount of
GC.
Sometimes we have more data than can fit into BlockCache and it is cause a high
rate of evictions.
, you choose to not cache a block which we would have otherwise chosen to cache
under the assumption that this "churn": to cache the N+1th block, we have to
evict the Nth block.
See the picture in attachment with test below. Requests per second is higher,
GC is lower.
The key point of the code:
Added the parameter: *hbase.lru.cache.data.block.percent* which by default = 100
But if we set it 0-99, then will work the next logic:
{code:java}
public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean inMemory)
{
if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
return;
...
// the same code as usual
}
{code}
Descriptions of the test:
4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
4 RegionServers
4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
Total BlockCache Size = 48 Gb (8 % of data in HFiles)
Random read in 20 threads
I am going to make Pull Request, hope it is right way to make some contribution
in this cool product.
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png, cmp.png,
> evict_BC100_vs_BC23.png, read_requests_100pBC_vs_23pBC.png
>
>
> Hi!
> I first time here, correct me please if something wrong.
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions. In this case we can skip cache a block N and insted
> cache the N+1th block. Anyway we would evict N block quite soon and that why
> that skipping good for performance.
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 1-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Description:
Hi!
I first time here, correct me please if something wrong.
I want propose how to improve performance when data in HFiles much more than
BlockChache (usual story in BigData). The idea - caching only part of DATA
blocks. It is good becouse LruBlockCache starts to work and save huge amount of
GC.
Sometimes we have more data than can fit into BlockCache and it is cause a high
rate of evictions.
, you choose to not cache a block which we would have otherwise chosen to cache
under the assumption that this "churn": to cache the N+1th block, we have to
evict the Nth block.
See the picture in attachment with test below. Requests per second is higher,
GC is lower.
The key point of the code:
Added the parameter: *hbase.lru.cache.data.block.percent* which by default = 100
But if we set it 0-99, then will work the next logic:
{code:java}
public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean inMemory)
{
if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
return;
...
// the same code as usual
}
{code}
Descriptions of the test:
4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
4 RegionServers
4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
Total BlockCache Size = 48 Gb (8 % of data in HFiles)
Random read in 20 threads
I am going to make Pull Request, hope it is right way to make some contribution
in this cool product.
was:
НГHi!
I first time here, correct me please if something wrong.
I want propose how to improve performance when data in HFiles much more than
BlockChache (usual story in BigData). The idea - caching only part of DATA
blocks. It is good becouse LruBlockCache starts to work and save huge amount of
GC. See the picture in attachment with test below. Requests per second is
higher, GC is lower.
The key point of the code:
Added the parameter: *hbase.lru.cache.data.block.percent* which by default = 100
But if we set it 0-99, then will work the next logic:
{code:java}
public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean inMemory)
{
if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
return;
...
// the same code as usual
}
{code}
Descriptions of the test:
4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
4 RegionServers
4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
Total BlockCache Size = 48 Gb (8 % of data in HFiles)
Random read in 20 threads
I am going to make Pull Request, hope it is right way to make some contribution
in this cool product.
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png, cmp.png,
> evict_BC100_vs_BC23.png, read_requests_100pBC_vs_23pBC.png
>
>
> Hi!
> I first time here, correct me please if something wrong.
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC.
> Sometimes we have more data than can fit into BlockCache and it is cause a
> high rate of evictions.
> , you choose to not cache a block which we would have otherwise chosen to
> cache under the assumption that this "churn": to cache the N+1th block, we
> have to evict the Nth block.
> See the picture in attachment with test below. Requests per second is higher,
> GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 0-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random
[jira] [Updated] (HBASE-23887) BlockCache performance improve by reduce eviction rate
[
https://issues.apache.org/jira/browse/HBASE-23887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Danil Lipovoy updated HBASE-23887:
--
Summary: BlockCache performance improve by reduce eviction rate (was:
BlockCache performance improve by avoid of chache)
> BlockCache performance improve by reduce eviction rate
> --
>
> Key: HBASE-23887
> URL: https://issues.apache.org/jira/browse/HBASE-23887
> Project: HBase
> Issue Type: Improvement
> Components: BlockCache, Performance
>Reporter: Danil Lipovoy
>Priority: Minor
> Attachments: 1582787018434_rs_metrics.jpg,
> 1582801838065_rs_metrics_new.png, BC_LongRun.png, cmp.png,
> evict_BC100_vs_BC23.png, read_requests_100pBC_vs_23pBC.png
>
>
> НГHi!
> I first time here, correct me please if something wrong.
> I want propose how to improve performance when data in HFiles much more than
> BlockChache (usual story in BigData). The idea - caching only part of DATA
> blocks. It is good becouse LruBlockCache starts to work and save huge amount
> of GC. See the picture in attachment with test below. Requests per second is
> higher, GC is lower.
>
> The key point of the code:
> Added the parameter: *hbase.lru.cache.data.block.percent* which by default =
> 100
>
> But if we set it 0-99, then will work the next logic:
>
>
> {code:java}
> public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean
> inMemory) {
> if (cacheDataBlockPercent != 100 && buf.getBlockType().isData())
> if (cacheKey.getOffset() % 100 >= cacheDataBlockPercent)
> return;
> ...
> // the same code as usual
> }
> {code}
>
>
> Descriptions of the test:
> 4 nodes E5-2698 v4 @ 2.20GHz, 700 Gb Mem.
> 4 RegionServers
> 4 tables by 64 regions by 1.88 Gb data in each = 600 Gb total (only FAST_DIFF)
> Total BlockCache Size = 48 Gb (8 % of data in HFiles)
> Random read in 20 threads
>
> I am going to make Pull Request, hope it is right way to make some
> contribution in this cool product.
>
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