Re: clarification on 100k tombstone limit in indexes
add an additional integer column to the partition key (making it a composite partition key if it isn't already). When inserting, randomly pick a value between, say, 0 and 10 to use for this column -- Due to the low cardinality of bucket (only 10), there is no guarantee that the partitions would be distributed evenly. But it's better than nothing. Alternatively, instead of using a random number, you could hash the other key components and use the lowest bits for the value. This has the advantage of being deterministic -- Does it work with VNodes, where tokens are split in 256 ranges and shuffled in all nodes ? On Tue, Aug 12, 2014 at 7:39 PM, Tyler Hobbs ty...@datastax.com wrote: On Mon, Aug 11, 2014 at 4:17 PM, Ian Rose ianr...@fullstory.com wrote: You better off create a manuel reverse-index to track modification date, something like this -- I had considered an approach like this but my concern is that for any given minute *all* of the updates will be handled by a single node, right? For example, if the minute_bucket is 2739 then for that one minute, every single item update will flow to the node at HASH(2739). Assuming I am thinking about that right, that seemed like a potential scaling bottleneck, which scared me off that approach. If you're concerned about bottlenecking on one node (or set of replicas) during the minute, add an additional integer column to the partition key (making it a composite partition key if it isn't already). When inserting, randomly pick a value between, say, 0 and 10 to use for this column. When reading, read all 10 partitions and merge them. (Alternatively, instead of using a random number, you could hash the other key components and use the lowest bits for the value. This has the advantage of being deterministic.) -- Tyler Hobbs DataStax http://datastax.com/
Re: clarification on 100k tombstone limit in indexes
On Wed, Aug 13, 2014 at 4:35 AM, DuyHai Doan doanduy...@gmail.com wrote: add an additional integer column to the partition key (making it a composite partition key if it isn't already). When inserting, randomly pick a value between, say, 0 and 10 to use for this column -- Due to the low cardinality of bucket (only 10), there is no guarantee that the partitions would be distributed evenly. But it's better than nothing. It's important to think about it probablistically, i.e. what is the probability that all ten partitions belong to the same node? If you have a ten node cluster (assume RF=1 for simplicity), there's a 1/10^9 (one in a billion) chance that a single node is the owner for all partitions. So it's quite a bit better than nothing. If you want to improve your odds, bump the number up. But, keep in mind that it's a balance, because reads become more expensive. Alternatively, instead of using a random number, you could hash the other key components and use the lowest bits for the value. This has the advantage of being deterministic -- Does it work with VNodes, where tokens are split in 256 ranges and shuffled in all nodes ? Yes, it works perfectly fine with vnodes. -- Tyler Hobbs DataStax http://datastax.com/
Re: clarification on 100k tombstone limit in indexes
Hello Ian So that way each index entry *will* have quite a few entries and the index as a whole won't grow too big. Is my thinking correct here? -- In this case yes. Do not forget that for each date value, there will be 1 corresponding index value + 10 updates. If you have an approximate count for a few entries, a quick maths should give you an idea about how large the index partition is I had considered an approach like this but my concern is that for any given minute *all* of the updates will be handled by a single node, right? -- If you time resolution is a minute, yes it will be a problem. And depending on the insert rate, it can become a quickly a bottle neck during this minute. The manual index approach suffers a lot from bottleneck issue for heavy workload, that's the main reason they implement a distributed secondary index. There is no free lunch though. What you gain in term of control and tuning with the manual index, you loose on the load distribution side. On Mon, Aug 11, 2014 at 11:17 PM, Ian Rose ianr...@fullstory.com wrote: Hi DuyHai, Thanks for the detailed response! A few responses below: On a side node, your usage of secondary index is not the best one. Indeed, indexing the update date will lead to a situation where for one date, you'll mostly have one or a few matching items (assuming that the update date resolution is small enough and update rate is not intense). -- I should have mentioned this original (slipped my mind) but to deal specifically with this problem I had planned to use a timestamp with a resolution of 1 minute (like your minute_bucket). So that way each index entry *will* have quite a few entries and the index as a whole won't grow too big. Is my thinking correct here? You better off create a manuel reverse-index to track modification date, something like this -- I had considered an approach like this but my concern is that for any given minute *all* of the updates will be handled by a single node, right? For example, if the minute_bucket is 2739 then for that one minute, every single item update will flow to the node at HASH(2739). Assuming I am thinking about that right, that seemed like a potential scaling bottleneck, which scared me off that approach. Cheers, Ian On Sun, Aug 10, 2014 at 5:20 PM, DuyHai Doan doanduy...@gmail.com wrote: Hello Ian It sounds like this 100k limit is, indeed, a global limit as opposed to a per-row limit --The threshold applies to each REQUEST, not partition or globally. The threshold does not apply to a partition (physical row) simply because in one request you can fetch data from many partitions (multi get slice). There was a JIRA about this here: https://issues.apache.org/jira/browse/CASSANDRA-6865 Are these tombstones ever GCed out of the index? -- Yes they are, during compactions of the index column family. How frequently? -- That's the real pain. Indeed you do not have any control on the tuning of secondary index CF compaction. As far as I know, the compaction settings (strategy, min/max thresholds...) inherits from the one of the base table Now, by looking very fast into your data model, it seems that you have a skinny partition patter. Since you mentioned that the date is updated only 10 times max, you should not run into the tombstonne threshold issue. On a side node, your usage of secondary index is not the best one. Indeed, indexing the update date will lead to a situation where for one date, you'll mostly have one or a few matching items (assuming that the update date resolution is small enough and update rate is not intense). It is the high-cardinality scenario to be avoided ( http://www.datastax.com/documentation/cql/3.0/cql/ddl/ddl_when_use_index_c.html). Plus, the query on the index (find all items where last_updated [now - 30 minutes]) makes things worse since it is not an exact match but inequality. You better off create a manuel reverse-index to track modification date, something like this: CREATE TABLE last_updated_item ( minute_bucket int, // format MMDDHHmm last_update_date timestamp, item_id ascii, PRIMARY KEY(minute_bucket, last_update_date) ); The last_update_date column is quite self-explanatory. The minute_bucket is trickier. The idea is to split ranges on 30 minutes into buckets. 00:00 to 00:30 is bucket 1, 00:30 to 01:00 is bucket 2 and so on. For a whole day, you'd have 48 buckets. We need to put data into buckets to avoid ultra wide rows since you mentioned that there are 10 items (so 10 updates) / sec. Of course, 30 mins is just an exemple, you can tune it down to a window of 5 minutes or 1 minute, depending on the insertion rate. On Sun, Aug 10, 2014 at 10:02 PM, Ian Rose ianr...@fullstory.com wrote: Hi Mark - Thanks for the clarification but as I'm not too familiar with the nuts bolts of Cassandra I'm not sure how to apply that info to my current situation. It sounds like this
Re: clarification on 100k tombstone limit in indexes
Makes sense - thanks again! On Tue, Aug 12, 2014 at 9:45 AM, DuyHai Doan doanduy...@gmail.com wrote: Hello Ian So that way each index entry *will* have quite a few entries and the index as a whole won't grow too big. Is my thinking correct here? -- In this case yes. Do not forget that for each date value, there will be 1 corresponding index value + 10 updates. If you have an approximate count for a few entries, a quick maths should give you an idea about how large the index partition is I had considered an approach like this but my concern is that for any given minute *all* of the updates will be handled by a single node, right? -- If you time resolution is a minute, yes it will be a problem. And depending on the insert rate, it can become a quickly a bottle neck during this minute. The manual index approach suffers a lot from bottleneck issue for heavy workload, that's the main reason they implement a distributed secondary index. There is no free lunch though. What you gain in term of control and tuning with the manual index, you loose on the load distribution side. On Mon, Aug 11, 2014 at 11:17 PM, Ian Rose ianr...@fullstory.com wrote: Hi DuyHai, Thanks for the detailed response! A few responses below: On a side node, your usage of secondary index is not the best one. Indeed, indexing the update date will lead to a situation where for one date, you'll mostly have one or a few matching items (assuming that the update date resolution is small enough and update rate is not intense). -- I should have mentioned this original (slipped my mind) but to deal specifically with this problem I had planned to use a timestamp with a resolution of 1 minute (like your minute_bucket). So that way each index entry *will* have quite a few entries and the index as a whole won't grow too big. Is my thinking correct here? You better off create a manuel reverse-index to track modification date, something like this -- I had considered an approach like this but my concern is that for any given minute *all* of the updates will be handled by a single node, right? For example, if the minute_bucket is 2739 then for that one minute, every single item update will flow to the node at HASH(2739). Assuming I am thinking about that right, that seemed like a potential scaling bottleneck, which scared me off that approach. Cheers, Ian On Sun, Aug 10, 2014 at 5:20 PM, DuyHai Doan doanduy...@gmail.com wrote: Hello Ian It sounds like this 100k limit is, indeed, a global limit as opposed to a per-row limit --The threshold applies to each REQUEST, not partition or globally. The threshold does not apply to a partition (physical row) simply because in one request you can fetch data from many partitions (multi get slice). There was a JIRA about this here: https://issues.apache.org/jira/browse/CASSANDRA-6865 Are these tombstones ever GCed out of the index? -- Yes they are, during compactions of the index column family. How frequently? -- That's the real pain. Indeed you do not have any control on the tuning of secondary index CF compaction. As far as I know, the compaction settings (strategy, min/max thresholds...) inherits from the one of the base table Now, by looking very fast into your data model, it seems that you have a skinny partition patter. Since you mentioned that the date is updated only 10 times max, you should not run into the tombstonne threshold issue. On a side node, your usage of secondary index is not the best one. Indeed, indexing the update date will lead to a situation where for one date, you'll mostly have one or a few matching items (assuming that the update date resolution is small enough and update rate is not intense). It is the high-cardinality scenario to be avoided ( http://www.datastax.com/documentation/cql/3.0/cql/ddl/ddl_when_use_index_c.html). Plus, the query on the index (find all items where last_updated [now - 30 minutes]) makes things worse since it is not an exact match but inequality. You better off create a manuel reverse-index to track modification date, something like this: CREATE TABLE last_updated_item ( minute_bucket int, // format MMDDHHmm last_update_date timestamp, item_id ascii, PRIMARY KEY(minute_bucket, last_update_date) ); The last_update_date column is quite self-explanatory. The minute_bucket is trickier. The idea is to split ranges on 30 minutes into buckets. 00:00 to 00:30 is bucket 1, 00:30 to 01:00 is bucket 2 and so on. For a whole day, you'd have 48 buckets. We need to put data into buckets to avoid ultra wide rows since you mentioned that there are 10 items (so 10 updates) / sec. Of course, 30 mins is just an exemple, you can tune it down to a window of 5 minutes or 1 minute, depending on the insertion rate. On Sun, Aug 10, 2014 at 10:02 PM, Ian Rose ianr...@fullstory.com wrote: Hi Mark - Thanks for the clarification but as I'm not too
Re: clarification on 100k tombstone limit in indexes
On Mon, Aug 11, 2014 at 4:17 PM, Ian Rose ianr...@fullstory.com wrote: You better off create a manuel reverse-index to track modification date, something like this -- I had considered an approach like this but my concern is that for any given minute *all* of the updates will be handled by a single node, right? For example, if the minute_bucket is 2739 then for that one minute, every single item update will flow to the node at HASH(2739). Assuming I am thinking about that right, that seemed like a potential scaling bottleneck, which scared me off that approach. If you're concerned about bottlenecking on one node (or set of replicas) during the minute, add an additional integer column to the partition key (making it a composite partition key if it isn't already). When inserting, randomly pick a value between, say, 0 and 10 to use for this column. When reading, read all 10 partitions and merge them. (Alternatively, instead of using a random number, you could hash the other key components and use the lowest bits for the value. This has the advantage of being deterministic.) -- Tyler Hobbs DataStax http://datastax.com/
Re: clarification on 100k tombstone limit in indexes
Hi DuyHai, Thanks for the detailed response! A few responses below: On a side node, your usage of secondary index is not the best one. Indeed, indexing the update date will lead to a situation where for one date, you'll mostly have one or a few matching items (assuming that the update date resolution is small enough and update rate is not intense). -- I should have mentioned this original (slipped my mind) but to deal specifically with this problem I had planned to use a timestamp with a resolution of 1 minute (like your minute_bucket). So that way each index entry *will* have quite a few entries and the index as a whole won't grow too big. Is my thinking correct here? You better off create a manuel reverse-index to track modification date, something like this -- I had considered an approach like this but my concern is that for any given minute *all* of the updates will be handled by a single node, right? For example, if the minute_bucket is 2739 then for that one minute, every single item update will flow to the node at HASH(2739). Assuming I am thinking about that right, that seemed like a potential scaling bottleneck, which scared me off that approach. Cheers, Ian On Sun, Aug 10, 2014 at 5:20 PM, DuyHai Doan doanduy...@gmail.com wrote: Hello Ian It sounds like this 100k limit is, indeed, a global limit as opposed to a per-row limit --The threshold applies to each REQUEST, not partition or globally. The threshold does not apply to a partition (physical row) simply because in one request you can fetch data from many partitions (multi get slice). There was a JIRA about this here: https://issues.apache.org/jira/browse/CASSANDRA-6865 Are these tombstones ever GCed out of the index? -- Yes they are, during compactions of the index column family. How frequently? -- That's the real pain. Indeed you do not have any control on the tuning of secondary index CF compaction. As far as I know, the compaction settings (strategy, min/max thresholds...) inherits from the one of the base table Now, by looking very fast into your data model, it seems that you have a skinny partition patter. Since you mentioned that the date is updated only 10 times max, you should not run into the tombstonne threshold issue. On a side node, your usage of secondary index is not the best one. Indeed, indexing the update date will lead to a situation where for one date, you'll mostly have one or a few matching items (assuming that the update date resolution is small enough and update rate is not intense). It is the high-cardinality scenario to be avoided ( http://www.datastax.com/documentation/cql/3.0/cql/ddl/ddl_when_use_index_c.html). Plus, the query on the index (find all items where last_updated [now - 30 minutes]) makes things worse since it is not an exact match but inequality. You better off create a manuel reverse-index to track modification date, something like this: CREATE TABLE last_updated_item ( minute_bucket int, // format MMDDHHmm last_update_date timestamp, item_id ascii, PRIMARY KEY(minute_bucket, last_update_date) ); The last_update_date column is quite self-explanatory. The minute_bucket is trickier. The idea is to split ranges on 30 minutes into buckets. 00:00 to 00:30 is bucket 1, 00:30 to 01:00 is bucket 2 and so on. For a whole day, you'd have 48 buckets. We need to put data into buckets to avoid ultra wide rows since you mentioned that there are 10 items (so 10 updates) / sec. Of course, 30 mins is just an exemple, you can tune it down to a window of 5 minutes or 1 minute, depending on the insertion rate. On Sun, Aug 10, 2014 at 10:02 PM, Ian Rose ianr...@fullstory.com wrote: Hi Mark - Thanks for the clarification but as I'm not too familiar with the nuts bolts of Cassandra I'm not sure how to apply that info to my current situation. It sounds like this 100k limit is, indeed, a global limit as opposed to a per-row limit. Are these tombstones ever GCed out of the index? How frequently? If not, then it seems like *any* index is at risk of reaching this tipping point; it's just that indexes on frequently updated columns will reach this pointer faster the indexes on rarely updated columns. Basically I'm trying to get some kind of sense for what frequently updated http://www.datastax.com/documentation/cql/3.0/cql/ddl/ddl_when_use_index_c.html means quantitatively. As written, the docs make it sound dangerous to create an index on a column that is *ever* deleted or updated since there is no sense of how frequent is too frequent. Cheers, Ian On Sun, Aug 10, 2014 at 3:02 PM, Mark Reddy mark.re...@boxever.com wrote: Hi Ian, The issues here, which relates to normal and index column families, is scanning over a large number of tombstones can cause Cassandra to fall over due to increased GC pressure. This pressure is caused because tombstones will create DeletedColumn objects which consume heap.
Re: clarification on 100k tombstone limit in indexes
Hi Ian, The issues here, which relates to normal and index column families, is scanning over a large number of tombstones can cause Cassandra to fall over due to increased GC pressure. This pressure is caused because tombstones will create DeletedColumn objects which consume heap. Also these DeletedColumn objects will have to be serialized and sent back to the coordinator, thus increasing your response times. Take for example a row that does deletes and you query it with a limit of 100. In a worst case scenario you could end up reading say 50k tombstones to reach the 100 'live' column limit, all of which has to be put on heap and then sent over the wire to the coordinator. This would be considered a Cassandra anti-pattern.[1] With that in mind there was a debug warning added to 1.2 to inform the user when they were querying a row with 1000 tombstones [2]. Then in 2.0 the action was taken to drop requests reaching 100k tombstones[3] rather than just printing out a warning. This is a safety measure, as it is not advised to perform such a query and is a result of most people 'doing it wrong'. For those people who understand the risk of scanning over large numbers of tombstones there is a configuration option in the cassandra.yaml to increase this threshold, tombstone_failure_threshold.[4] Mark [1] http://www.datastax.com/dev/blog/cassandra-anti-patterns-queues-and-queue-like-datasets [2] https://issues.apache.org/jira/browse/CASSANDRA-6042 [3] https://issues.apache.org/jira/browse/CASSANDRA-6117 [4] https://github.com/jbellis/cassandra/blob/4ac18ae805d28d8f4cb44b42e2244bfa6d2875e1/conf/cassandra.yaml#L407-L417 On Sun, Aug 10, 2014 at 7:19 PM, Ian Rose ianr...@fullstory.com wrote: Hi - On this page ( http://www.datastax.com/documentation/cql/3.0/cql/ddl/ddl_when_use_index_c.html), the docs state: Do not use an index [...] On a frequently updated or deleted column and *Problems using an index on a frequently updated or deleted column*¶ http://www.datastax.com/documentation/cql/3.0/cql/ddl/ddl_when_use_index_c.html?scroll=concept_ds_sgh_yzz_zj__upDatIndx Cassandra stores tombstones in the index until the tombstone limit reaches 100K cells. After exceeding the tombstone limit, the query that uses the indexed value will fail. I'm afraid I don't really understand this limit from its (brief) description. I also saw this recent thread http://mail-archives.apache.org/mod_mbox/cassandra-user/201403.mbox/%3CCABNXB2Bf4aeoDVpMNOxJ_e7aDez2EuZswMJx=jWfb8=oyo4...@mail.gmail.com%3E but I'm afraid it didn't help me much... *SHORT VERSION* If I have tens or hundreds of thousands of rows in a keyspace, where every row has an indexed column that is updated O(10) times during the lifetime of each row, is that going to cause problems for me? If that 100k limit is *per row* then I should be fine but if that 100k limit is *per keyspace* then I'd definitely exceed it quickly. *FULL EXPLANATION* In our system, items are created at a rate of ~10/sec. Each item is updated ~10 times over the next few minutes (although in rare cases the number of updates, and the duration, might be several times as long). Once the last update is received for an item, we select it from Cassandra, process the data, then delete the entire row. The tricky bit is that sometimes (maybe 30-40% of the time) we don't actually know when the last update has been received so we use a timeout: if an item hasn't been updated for 30 minutes, then we assume it is done and should process it as before (select, then delete). So I am trying to design a schema that will allow for efficient queries of the form find me all items that have not been updated in the past 30 minutes. We plan to call this query once a minute. Here is my tentative schema: CREATE TABLE items ( item_id ascii, last_updated timestamp, item_data listblob, PRIMARY KEY (item_id) ) plus an index on last_updated. So updates to an existing item would just be lookup by item_id, append new data to item_data, and set last_updated to now. And queries to find items that have timed out would use the index on last_updated: find all items where last_updated [now - 30 minutes]. Assuming, that is, that the aforementioned 100k tombstone limit won't bring this index crashing to a halt... Any clarification on this limit and/or suggestions on a better way to model/implement this system would be greatly appreciated! Cheers, Ian
Re: clarification on 100k tombstone limit in indexes
Hi Mark - Thanks for the clarification but as I'm not too familiar with the nuts bolts of Cassandra I'm not sure how to apply that info to my current situation. It sounds like this 100k limit is, indeed, a global limit as opposed to a per-row limit. Are these tombstones ever GCed out of the index? How frequently? If not, then it seems like *any* index is at risk of reaching this tipping point; it's just that indexes on frequently updated columns will reach this pointer faster the indexes on rarely updated columns. Basically I'm trying to get some kind of sense for what frequently updated http://www.datastax.com/documentation/cql/3.0/cql/ddl/ddl_when_use_index_c.html means quantitatively. As written, the docs make it sound dangerous to create an index on a column that is *ever* deleted or updated since there is no sense of how frequent is too frequent. Cheers, Ian On Sun, Aug 10, 2014 at 3:02 PM, Mark Reddy mark.re...@boxever.com wrote: Hi Ian, The issues here, which relates to normal and index column families, is scanning over a large number of tombstones can cause Cassandra to fall over due to increased GC pressure. This pressure is caused because tombstones will create DeletedColumn objects which consume heap. Also these DeletedColumn objects will have to be serialized and sent back to the coordinator, thus increasing your response times. Take for example a row that does deletes and you query it with a limit of 100. In a worst case scenario you could end up reading say 50k tombstones to reach the 100 'live' column limit, all of which has to be put on heap and then sent over the wire to the coordinator. This would be considered a Cassandra anti-pattern.[1] With that in mind there was a debug warning added to 1.2 to inform the user when they were querying a row with 1000 tombstones [2]. Then in 2.0 the action was taken to drop requests reaching 100k tombstones[3] rather than just printing out a warning. This is a safety measure, as it is not advised to perform such a query and is a result of most people 'doing it wrong'. For those people who understand the risk of scanning over large numbers of tombstones there is a configuration option in the cassandra.yaml to increase this threshold, tombstone_failure_threshold.[4] Mark [1] http://www.datastax.com/dev/blog/cassandra-anti-patterns-queues-and-queue-like-datasets [2] https://issues.apache.org/jira/browse/CASSANDRA-6042 [3] https://issues.apache.org/jira/browse/CASSANDRA-6117 [4] https://github.com/jbellis/cassandra/blob/4ac18ae805d28d8f4cb44b42e2244bfa6d2875e1/conf/cassandra.yaml#L407-L417 On Sun, Aug 10, 2014 at 7:19 PM, Ian Rose ianr...@fullstory.com wrote: Hi - On this page ( http://www.datastax.com/documentation/cql/3.0/cql/ddl/ddl_when_use_index_c.html), the docs state: Do not use an index [...] On a frequently updated or deleted column and *Problems using an index on a frequently updated or deleted column*¶ http://www.datastax.com/documentation/cql/3.0/cql/ddl/ddl_when_use_index_c.html?scroll=concept_ds_sgh_yzz_zj__upDatIndx Cassandra stores tombstones in the index until the tombstone limit reaches 100K cells. After exceeding the tombstone limit, the query that uses the indexed value will fail. I'm afraid I don't really understand this limit from its (brief) description. I also saw this recent thread http://mail-archives.apache.org/mod_mbox/cassandra-user/201403.mbox/%3CCABNXB2Bf4aeoDVpMNOxJ_e7aDez2EuZswMJx=jWfb8=oyo4...@mail.gmail.com%3E but I'm afraid it didn't help me much... *SHORT VERSION* If I have tens or hundreds of thousands of rows in a keyspace, where every row has an indexed column that is updated O(10) times during the lifetime of each row, is that going to cause problems for me? If that 100k limit is *per row* then I should be fine but if that 100k limit is *per keyspace* then I'd definitely exceed it quickly. *FULL EXPLANATION* In our system, items are created at a rate of ~10/sec. Each item is updated ~10 times over the next few minutes (although in rare cases the number of updates, and the duration, might be several times as long). Once the last update is received for an item, we select it from Cassandra, process the data, then delete the entire row. The tricky bit is that sometimes (maybe 30-40% of the time) we don't actually know when the last update has been received so we use a timeout: if an item hasn't been updated for 30 minutes, then we assume it is done and should process it as before (select, then delete). So I am trying to design a schema that will allow for efficient queries of the form find me all items that have not been updated in the past 30 minutes. We plan to call this query once a minute. Here is my tentative schema: CREATE TABLE items ( item_id ascii, last_updated timestamp, item_data listblob, PRIMARY KEY (item_id) ) plus an index on last_updated. So updates to an existing item
Re: clarification on 100k tombstone limit in indexes
Hi Ian Are these tombstones ever GCed out of the index? How frequently? Yes, tombstones are removed after the time specified by gc_grace_seconds has elapsed, which by default is 10 days and is configurable. Knowing and understanding how Cassandra handles distributed deletes is key to designing an efficient schema if you plan to delete often. There are lots of resources on how deletes are handled in Cassandra, take a look at these links for example: http://wiki.apache.org/cassandra/DistributedDeletes http://www.datastax.com/documentation/cassandra/2.0/cassandra/dml/dml_about_deletes_c.html Mark On Sun, Aug 10, 2014 at 9:02 PM, Ian Rose ianr...@fullstory.com wrote: Hi Mark - Thanks for the clarification but as I'm not too familiar with the nuts bolts of Cassandra I'm not sure how to apply that info to my current situation. It sounds like this 100k limit is, indeed, a global limit as opposed to a per-row limit. Are these tombstones ever GCed out of the index? How frequently? If not, then it seems like *any* index is at risk of reaching this tipping point; it's just that indexes on frequently updated columns will reach this pointer faster the indexes on rarely updated columns. Basically I'm trying to get some kind of sense for what frequently updated http://www.datastax.com/documentation/cql/3.0/cql/ddl/ddl_when_use_index_c.html means quantitatively. As written, the docs make it sound dangerous to create an index on a column that is *ever* deleted or updated since there is no sense of how frequent is too frequent. Cheers, Ian On Sun, Aug 10, 2014 at 3:02 PM, Mark Reddy mark.re...@boxever.com wrote: Hi Ian, The issues here, which relates to normal and index column families, is scanning over a large number of tombstones can cause Cassandra to fall over due to increased GC pressure. This pressure is caused because tombstones will create DeletedColumn objects which consume heap. Also these DeletedColumn objects will have to be serialized and sent back to the coordinator, thus increasing your response times. Take for example a row that does deletes and you query it with a limit of 100. In a worst case scenario you could end up reading say 50k tombstones to reach the 100 'live' column limit, all of which has to be put on heap and then sent over the wire to the coordinator. This would be considered a Cassandra anti-pattern.[1] With that in mind there was a debug warning added to 1.2 to inform the user when they were querying a row with 1000 tombstones [2]. Then in 2.0 the action was taken to drop requests reaching 100k tombstones[3] rather than just printing out a warning. This is a safety measure, as it is not advised to perform such a query and is a result of most people 'doing it wrong'. For those people who understand the risk of scanning over large numbers of tombstones there is a configuration option in the cassandra.yaml to increase this threshold, tombstone_failure_threshold.[4] Mark [1] http://www.datastax.com/dev/blog/cassandra-anti-patterns-queues-and-queue-like-datasets [2] https://issues.apache.org/jira/browse/CASSANDRA-6042 [3] https://issues.apache.org/jira/browse/CASSANDRA-6117 [4] https://github.com/jbellis/cassandra/blob/4ac18ae805d28d8f4cb44b42e2244bfa6d2875e1/conf/cassandra.yaml#L407-L417 On Sun, Aug 10, 2014 at 7:19 PM, Ian Rose ianr...@fullstory.com wrote: Hi - On this page ( http://www.datastax.com/documentation/cql/3.0/cql/ddl/ddl_when_use_index_c.html), the docs state: Do not use an index [...] On a frequently updated or deleted column and *Problems using an index on a frequently updated or deleted column*¶ http://www.datastax.com/documentation/cql/3.0/cql/ddl/ddl_when_use_index_c.html?scroll=concept_ds_sgh_yzz_zj__upDatIndx Cassandra stores tombstones in the index until the tombstone limit reaches 100K cells. After exceeding the tombstone limit, the query that uses the indexed value will fail. I'm afraid I don't really understand this limit from its (brief) description. I also saw this recent thread http://mail-archives.apache.org/mod_mbox/cassandra-user/201403.mbox/%3CCABNXB2Bf4aeoDVpMNOxJ_e7aDez2EuZswMJx=jWfb8=oyo4...@mail.gmail.com%3E but I'm afraid it didn't help me much... *SHORT VERSION* If I have tens or hundreds of thousands of rows in a keyspace, where every row has an indexed column that is updated O(10) times during the lifetime of each row, is that going to cause problems for me? If that 100k limit is *per row* then I should be fine but if that 100k limit is *per keyspace* then I'd definitely exceed it quickly. *FULL EXPLANATION* In our system, items are created at a rate of ~10/sec. Each item is updated ~10 times over the next few minutes (although in rare cases the number of updates, and the duration, might be several times as long). Once the last update is received for an item, we select it from Cassandra, process the data, then delete the entire
Re: clarification on 100k tombstone limit in indexes
Hello Ian It sounds like this 100k limit is, indeed, a global limit as opposed to a per-row limit --The threshold applies to each REQUEST, not partition or globally. The threshold does not apply to a partition (physical row) simply because in one request you can fetch data from many partitions (multi get slice). There was a JIRA about this here: https://issues.apache.org/jira/browse/CASSANDRA-6865 Are these tombstones ever GCed out of the index? -- Yes they are, during compactions of the index column family. How frequently? -- That's the real pain. Indeed you do not have any control on the tuning of secondary index CF compaction. As far as I know, the compaction settings (strategy, min/max thresholds...) inherits from the one of the base table Now, by looking very fast into your data model, it seems that you have a skinny partition patter. Since you mentioned that the date is updated only 10 times max, you should not run into the tombstonne threshold issue. On a side node, your usage of secondary index is not the best one. Indeed, indexing the update date will lead to a situation where for one date, you'll mostly have one or a few matching items (assuming that the update date resolution is small enough and update rate is not intense). It is the high-cardinality scenario to be avoided ( http://www.datastax.com/documentation/cql/3.0/cql/ddl/ddl_when_use_index_c.html). Plus, the query on the index (find all items where last_updated [now - 30 minutes]) makes things worse since it is not an exact match but inequality. You better off create a manuel reverse-index to track modification date, something like this: CREATE TABLE last_updated_item ( minute_bucket int, // format MMDDHHmm last_update_date timestamp, item_id ascii, PRIMARY KEY(minute_bucket, last_update_date) ); The last_update_date column is quite self-explanatory. The minute_bucket is trickier. The idea is to split ranges on 30 minutes into buckets. 00:00 to 00:30 is bucket 1, 00:30 to 01:00 is bucket 2 and so on. For a whole day, you'd have 48 buckets. We need to put data into buckets to avoid ultra wide rows since you mentioned that there are 10 items (so 10 updates) / sec. Of course, 30 mins is just an exemple, you can tune it down to a window of 5 minutes or 1 minute, depending on the insertion rate. On Sun, Aug 10, 2014 at 10:02 PM, Ian Rose ianr...@fullstory.com wrote: Hi Mark - Thanks for the clarification but as I'm not too familiar with the nuts bolts of Cassandra I'm not sure how to apply that info to my current situation. It sounds like this 100k limit is, indeed, a global limit as opposed to a per-row limit. Are these tombstones ever GCed out of the index? How frequently? If not, then it seems like *any* index is at risk of reaching this tipping point; it's just that indexes on frequently updated columns will reach this pointer faster the indexes on rarely updated columns. Basically I'm trying to get some kind of sense for what frequently updated http://www.datastax.com/documentation/cql/3.0/cql/ddl/ddl_when_use_index_c.html means quantitatively. As written, the docs make it sound dangerous to create an index on a column that is *ever* deleted or updated since there is no sense of how frequent is too frequent. Cheers, Ian On Sun, Aug 10, 2014 at 3:02 PM, Mark Reddy mark.re...@boxever.com wrote: Hi Ian, The issues here, which relates to normal and index column families, is scanning over a large number of tombstones can cause Cassandra to fall over due to increased GC pressure. This pressure is caused because tombstones will create DeletedColumn objects which consume heap. Also these DeletedColumn objects will have to be serialized and sent back to the coordinator, thus increasing your response times. Take for example a row that does deletes and you query it with a limit of 100. In a worst case scenario you could end up reading say 50k tombstones to reach the 100 'live' column limit, all of which has to be put on heap and then sent over the wire to the coordinator. This would be considered a Cassandra anti-pattern.[1] With that in mind there was a debug warning added to 1.2 to inform the user when they were querying a row with 1000 tombstones [2]. Then in 2.0 the action was taken to drop requests reaching 100k tombstones[3] rather than just printing out a warning. This is a safety measure, as it is not advised to perform such a query and is a result of most people 'doing it wrong'. For those people who understand the risk of scanning over large numbers of tombstones there is a configuration option in the cassandra.yaml to increase this threshold, tombstone_failure_threshold.[4] Mark [1] http://www.datastax.com/dev/blog/cassandra-anti-patterns-queues-and-queue-like-datasets [2] https://issues.apache.org/jira/browse/CASSANDRA-6042 [3] https://issues.apache.org/jira/browse/CASSANDRA-6117 [4]
