Re: Phoenix as a source for Spark processing
Cool, that's a good find. Re-stating what you're seeing: the distribution of your HBase table (region splits) doesn't match an even distribution of the data in the HBase table. Some regions have more data than other regions. Typically, applications reading from HBase will launch workers based on the Region split points, or modulo some maximum number of "work items" (tasks, in your case, I'd guess). I'd take a look at the amount of data in HDFS for each region in your table, and see if you can find any skew. If there are large region(s), you can try to split them. Or, you can change the split threshold from the default of 10G (iirc) to a smaller number and let the system do it for you. On 3/15/18 5:49 AM, Stepan Migunov wrote: The table is about 300GB in hbase. I've done some more research and now my test is very simple - I'm tryng to calculate count of records of the table. No "distincts" and etc., just phoenixTableAsDataFrame(...).count(). And now I see the issue - Spark creates about 400 task (14 executors), starts calculation, speed is pretty good. Hbase shows about 1000 requests per second. But then Sparks stops tasks as completed. I can see that Spark have read only 20% of records, but completed 50% tasks. HBase shows only 100 requests per second. When Sparks "thinks" that 99% completed (only 5 tasks left), actually it read only 70% records. The rest of work will be done by 5 tasks with 1-2 request per second... Is the any way to force Spark distribute workload evenly? I have tried to pre-split my Phonix table (now it has about 1200 regions), but it did't help. -Original Message- From: Josh Elser [mailto:els...@apache.org] Sent: Friday, March 9, 2018 2:17 AM To: user@phoenix.apache.org Subject: Re: Phoenix as a source for Spark processing How large is each row in this case? Or, better yet, how large is the table in HBase? You're spreading out approximately 7 "clients" to each Regionserver fetching results (100/14). So, you should have pretty decent saturation from Spark into HBase. I'd be taking a look at the EXPLAIN plan for your SELECT DISTINCT to really understand what Phoenix is doing. For example, are you getting ample saturation of the resources that your servers have available (32core/128Gb memory is pretty good). Validating how busy Spark is actually keeping HBase, and how much time is spent transforming the data would be good. Or, another point, are you excessively scanning data in the system which you could otherwise preclude by a different rowkey structure via logic such as a skip-scan (which would be shown in the EXPLAIN plan). You may actually find that using the built-in UPSERT SELECT logic may out-perform the Spark integration since you aren't actually doing any transformation logic inside of Spark. On 3/5/18 3:14 PM, Stepan Migunov wrote: Hi Josh, thank you for response! Our cluster has 14 nodes (32 cores each/128 GB memory). The source Phoenix table contains about 1 billion records (100 columns). We start a Spark's job with about 100 executors. Spark executes SELECT from the source table (select 6 columns with DISTINCT) and writes down output to another Phoenix table. Expected that the target table will contains about 100 million records. HBase has 14 region servers, both tables salted with SALT_BUCKETS=42. Spark's job running via Yarn. -Original Message- From: Josh Elser [mailto:els...@apache.org] Sent: Monday, March 5, 2018 9:14 PM To: user@phoenix.apache.org Subject: Re: Phoenix as a source for Spark processing Hi Stepan, Can you better ballpark the Phoenix-Spark performance you've seen (e.g. how much hardware do you have, how many spark executors did you use, how many region servers)? Also, what versions of software are you using? I don't think there are any firm guidelines on how you can solve this problem, but you've found the tools available for you. * You can try Phoenix+Spark to run over the Phoenix tables in place * You can use Phoenix+Hive to offload the data into Hive for queries If Phoenix-Spark wasn't fast enough, I'd imagine using the Phoenix-Hive integration to query the data would be similarly not fast enough. It's possible that the bottleneck is something we could fix in the integration, or fix configuration of Spark and/or Phoenix. We'd need you to help quantify this better :) On 3/4/18 6:08 AM, Stepan Migunov wrote: In our software we need to combine fast interactive access to the data with quite complex data processing. I know that Phoenix intended for fast access, but hoped that also I could be able to use Phoenix as a source for complex processing with the Spark. Unfortunately, Phoenix + Spark shows very poor performance. E.g., querying big (about billion records) table with distinct takes about 2 hours. At the same time this task with Hive source takes a few minutes. Is it expected? Does it mean that Phoenix is absolutely not suitable for batch processing with s
RE: Phoenix as a source for Spark processing
The table is about 300GB in hbase. I've done some more research and now my test is very simple - I'm tryng to calculate count of records of the table. No "distincts" and etc., just phoenixTableAsDataFrame(...).count(). And now I see the issue - Spark creates about 400 task (14 executors), starts calculation, speed is pretty good. Hbase shows about 1000 requests per second. But then Sparks stops tasks as completed. I can see that Spark have read only 20% of records, but completed 50% tasks. HBase shows only 100 requests per second. When Sparks "thinks" that 99% completed (only 5 tasks left), actually it read only 70% records. The rest of work will be done by 5 tasks with 1-2 request per second... Is the any way to force Spark distribute workload evenly? I have tried to pre-split my Phonix table (now it has about 1200 regions), but it did't help. -Original Message- From: Josh Elser [mailto:els...@apache.org] Sent: Friday, March 9, 2018 2:17 AM To: user@phoenix.apache.org Subject: Re: Phoenix as a source for Spark processing How large is each row in this case? Or, better yet, how large is the table in HBase? You're spreading out approximately 7 "clients" to each Regionserver fetching results (100/14). So, you should have pretty decent saturation from Spark into HBase. I'd be taking a look at the EXPLAIN plan for your SELECT DISTINCT to really understand what Phoenix is doing. For example, are you getting ample saturation of the resources that your servers have available (32core/128Gb memory is pretty good). Validating how busy Spark is actually keeping HBase, and how much time is spent transforming the data would be good. Or, another point, are you excessively scanning data in the system which you could otherwise preclude by a different rowkey structure via logic such as a skip-scan (which would be shown in the EXPLAIN plan). You may actually find that using the built-in UPSERT SELECT logic may out-perform the Spark integration since you aren't actually doing any transformation logic inside of Spark. On 3/5/18 3:14 PM, Stepan Migunov wrote: > Hi Josh, thank you for response! > > Our cluster has 14 nodes (32 cores each/128 GB memory). The source > Phoenix table contains about 1 billion records (100 columns). We start > a Spark's job with about 100 executors. Spark executes SELECT from the > source table (select 6 columns with DISTINCT) and writes down output > to another Phoenix table. Expected that the target table will contains > about 100 million records. > HBase has 14 region servers, both tables salted with SALT_BUCKETS=42. > Spark's job running via Yarn. > > > -Original Message- > From: Josh Elser [mailto:els...@apache.org] > Sent: Monday, March 5, 2018 9:14 PM > To: user@phoenix.apache.org > Subject: Re: Phoenix as a source for Spark processing > > Hi Stepan, > > Can you better ballpark the Phoenix-Spark performance you've seen (e.g. > how much hardware do you have, how many spark executors did you use, > how many region servers)? Also, what versions of software are you using? > > I don't think there are any firm guidelines on how you can solve this > problem, but you've found the tools available for you. > > * You can try Phoenix+Spark to run over the Phoenix tables in place > * You can use Phoenix+Hive to offload the data into Hive for queries > > If Phoenix-Spark wasn't fast enough, I'd imagine using the > Phoenix-Hive integration to query the data would be similarly not fast > enough. > > It's possible that the bottleneck is something we could fix in the > integration, or fix configuration of Spark and/or Phoenix. We'd need > you to help quantify this better :) > > On 3/4/18 6:08 AM, Stepan Migunov wrote: >> In our software we need to combine fast interactive access to the >> data with quite complex data processing. I know that Phoenix intended >> for fast access, but hoped that also I could be able to use Phoenix >> as a source for complex processing with the Spark. Unfortunately, >> Phoenix + Spark shows very poor performance. E.g., querying big >> (about billion records) table with distinct takes about 2 hours. At >> the same time this task with Hive source takes a few minutes. Is it >> expected? Does it mean that Phoenix is absolutely not suitable for >> batch processing with spark and I should duplicate data to Hive and >> process it with Hive? >>
Re: Phoenix as a source for Spark processing
How large is each row in this case? Or, better yet, how large is the table in HBase? You're spreading out approximately 7 "clients" to each Regionserver fetching results (100/14). So, you should have pretty decent saturation from Spark into HBase. I'd be taking a look at the EXPLAIN plan for your SELECT DISTINCT to really understand what Phoenix is doing. For example, are you getting ample saturation of the resources that your servers have available (32core/128Gb memory is pretty good). Validating how busy Spark is actually keeping HBase, and how much time is spent transforming the data would be good. Or, another point, are you excessively scanning data in the system which you could otherwise preclude by a different rowkey structure via logic such as a skip-scan (which would be shown in the EXPLAIN plan). You may actually find that using the built-in UPSERT SELECT logic may out-perform the Spark integration since you aren't actually doing any transformation logic inside of Spark. On 3/5/18 3:14 PM, Stepan Migunov wrote: Hi Josh, thank you for response! Our cluster has 14 nodes (32 cores each/128 GB memory). The source Phoenix table contains about 1 billion records (100 columns). We start a Spark's job with about 100 executors. Spark executes SELECT from the source table (select 6 columns with DISTINCT) and writes down output to another Phoenix table. Expected that the target table will contains about 100 million records. HBase has 14 region servers, both tables salted with SALT_BUCKETS=42. Spark's job running via Yarn. -Original Message- From: Josh Elser [mailto:els...@apache.org] Sent: Monday, March 5, 2018 9:14 PM To: user@phoenix.apache.org Subject: Re: Phoenix as a source for Spark processing Hi Stepan, Can you better ballpark the Phoenix-Spark performance you've seen (e.g. how much hardware do you have, how many spark executors did you use, how many region servers)? Also, what versions of software are you using? I don't think there are any firm guidelines on how you can solve this problem, but you've found the tools available for you. * You can try Phoenix+Spark to run over the Phoenix tables in place * You can use Phoenix+Hive to offload the data into Hive for queries If Phoenix-Spark wasn't fast enough, I'd imagine using the Phoenix-Hive integration to query the data would be similarly not fast enough. It's possible that the bottleneck is something we could fix in the integration, or fix configuration of Spark and/or Phoenix. We'd need you to help quantify this better :) On 3/4/18 6:08 AM, Stepan Migunov wrote: In our software we need to combine fast interactive access to the data with quite complex data processing. I know that Phoenix intended for fast access, but hoped that also I could be able to use Phoenix as a source for complex processing with the Spark. Unfortunately, Phoenix + Spark shows very poor performance. E.g., querying big (about billion records) table with distinct takes about 2 hours. At the same time this task with Hive source takes a few minutes. Is it expected? Does it mean that Phoenix is absolutely not suitable for batch processing with spark and I should duplicate data to Hive and process it with Hive?
Re: Phoenix as a source for Spark processing
I would guess that Hive would always be capable of out-matching what HBase/Phoenix can do for this type of workload (bulk-transformation). That said, I'm not ready to tell you that you can't get the Phoenix-Spark integration better performing. See the other thread where you provide more details.. It's important to remember that Phoenix is designed to shine when you have workloads which require updates to a single row/column. The underlying I/O system is much different in HBase compared to Hive in order to server the random update use-case. On 3/7/18 4:08 AM, Stepan Migunov wrote: Some more details... We have done some simple tests to compare read/write possibility spark+hive and spark+phoenix. And now we have the following results: Copy table (with no any transformations) (about 800 million rec): Hive (TEZ) - 752 sec Spark: From Hive to Hive: 2463 sec From Phoenix to Hive - 13310 sec From Hive to Phoenix - > 30240 sec We use Spark 2.2.1; hbase 1.1.2, Phonix 4.13, Hive 2.1.1 So it seems that Spark + Phoenix led great performance degradation. Any thoughts? On 2018/03/04 11:08:56, Stepan Migunovwrote: In our software we need to combine fast interactive access to the data with quite complex data processing. I know that Phoenix intended for fast access, but hoped that also I could be able to use Phoenix as a source for complex processing with the Spark. Unfortunately, Phoenix + Spark shows very poor performance. E.g., querying big (about billion records) table with distinct takes about 2 hours. At the same time this task with Hive source takes a few minutes. Is it expected? Does it mean that Phoenix is absolutely not suitable for batch processing with spark and I should duplicate data to Hive and process it with Hive?
Re: Phoenix as a source for Spark processing
Some more details... We have done some simple tests to compare read/write possibility spark+hive and spark+phoenix. And now we have the following results: Copy table (with no any transformations) (about 800 million rec): Hive (TEZ) - 752 sec Spark: >From Hive to Hive: 2463 sec >From Phoenix to Hive - 13310 sec >From Hive to Phoenix - > 30240 sec We use Spark 2.2.1; hbase 1.1.2, Phonix 4.13, Hive 2.1.1 So it seems that Spark + Phoenix led great performance degradation. Any thoughts? On 2018/03/04 11:08:56, Stepan Migunovwrote: > In our software we need to combine fast interactive access to the data with > quite complex data processing. I know that Phoenix intended for fast access, > but hoped that also I could be able to use Phoenix as a source for complex > processing with the Spark. Unfortunately, Phoenix + Spark shows very poor > performance. E.g., querying big (about billion records) table with distinct > takes about 2 hours. At the same time this task with Hive source takes a few > minutes. Is it expected? Does it mean that Phoenix is absolutely not suitable > for batch processing with spark and I should duplicate data to Hive and > process it with Hive? >
RE: Phoenix as a source for Spark processing
Hi Josh, thank you for response! Our cluster has 14 nodes (32 cores each/128 GB memory). The source Phoenix table contains about 1 billion records (100 columns). We start a Spark's job with about 100 executors. Spark executes SELECT from the source table (select 6 columns with DISTINCT) and writes down output to another Phoenix table. Expected that the target table will contains about 100 million records. HBase has 14 region servers, both tables salted with SALT_BUCKETS=42. Spark's job running via Yarn. -Original Message- From: Josh Elser [mailto:els...@apache.org] Sent: Monday, March 5, 2018 9:14 PM To: user@phoenix.apache.org Subject: Re: Phoenix as a source for Spark processing Hi Stepan, Can you better ballpark the Phoenix-Spark performance you've seen (e.g. how much hardware do you have, how many spark executors did you use, how many region servers)? Also, what versions of software are you using? I don't think there are any firm guidelines on how you can solve this problem, but you've found the tools available for you. * You can try Phoenix+Spark to run over the Phoenix tables in place * You can use Phoenix+Hive to offload the data into Hive for queries If Phoenix-Spark wasn't fast enough, I'd imagine using the Phoenix-Hive integration to query the data would be similarly not fast enough. It's possible that the bottleneck is something we could fix in the integration, or fix configuration of Spark and/or Phoenix. We'd need you to help quantify this better :) On 3/4/18 6:08 AM, Stepan Migunov wrote: > In our software we need to combine fast interactive access to the data > with quite complex data processing. I know that Phoenix intended for fast > access, but hoped that also I could be able to use Phoenix as a source for > complex processing with the Spark. Unfortunately, Phoenix + Spark shows > very poor performance. E.g., querying big (about billion records) table > with distinct takes about 2 hours. At the same time this task with Hive > source takes a few minutes. Is it expected? Does it mean that Phoenix is > absolutely not suitable for batch processing with spark and I should > duplicate data to Hive and process it with Hive? >
Re: Phoenix as a source for Spark processing
Hi Stepan, Can you better ballpark the Phoenix-Spark performance you've seen (e.g. how much hardware do you have, how many spark executors did you use, how many region servers)? Also, what versions of software are you using? I don't think there are any firm guidelines on how you can solve this problem, but you've found the tools available for you. * You can try Phoenix+Spark to run over the Phoenix tables in place * You can use Phoenix+Hive to offload the data into Hive for queries If Phoenix-Spark wasn't fast enough, I'd imagine using the Phoenix-Hive integration to query the data would be similarly not fast enough. It's possible that the bottleneck is something we could fix in the integration, or fix configuration of Spark and/or Phoenix. We'd need you to help quantify this better :) On 3/4/18 6:08 AM, Stepan Migunov wrote: In our software we need to combine fast interactive access to the data with quite complex data processing. I know that Phoenix intended for fast access, but hoped that also I could be able to use Phoenix as a source for complex processing with the Spark. Unfortunately, Phoenix + Spark shows very poor performance. E.g., querying big (about billion records) table with distinct takes about 2 hours. At the same time this task with Hive source takes a few minutes. Is it expected? Does it mean that Phoenix is absolutely not suitable for batch processing with spark and I should duplicate data to Hive and process it with Hive?
Phoenix as a source for Spark processing
In our software we need to combine fast interactive access to the data with quite complex data processing. I know that Phoenix intended for fast access, but hoped that also I could be able to use Phoenix as a source for complex processing with the Spark. Unfortunately, Phoenix + Spark shows very poor performance. E.g., querying big (about billion records) table with distinct takes about 2 hours. At the same time this task with Hive source takes a few minutes. Is it expected? Does it mean that Phoenix is absolutely not suitable for batch processing with spark and I should duplicate data to Hive and process it with Hive?