manual forced compactions create more problems than they solve, if you have no evidence of tombstones in your selects (which seems odd, can you share some of the tracing output?), then I'm not sure what it would solve for you.
Compaction running could explain a high load, logs messages with ERRORS, WARN, GCInspector are all meaningful there, I suggest search jira for your version to see if there are any interesting bugs. On Tue, Dec 16, 2014 at 6:14 PM, Arne Claassen <a...@emotient.com> wrote: > > I just did a wide set of selects and ran across no tombstones. But while > on the subject of gc_grace_seconds, any reason, on a small cluster not to > set it to something low like a single day. It seems like 10 days is only > need to large clusters undergoing long partition splits, or am i > misunderstanding gc_grace_seconds. > > Now, given all that, does any of this explain a high load when the cluster > is idle? Is it compaction catching up and would manual forced compaction > alleviate that? > > thanks, > arne > > > On Dec 16, 2014, at 3:28 PM, Ryan Svihla <rsvi...@datastax.com> wrote: > > so a delete is really another write for gc_grace_seconds (default 10 > days), if you get enough tombstones it can make managing your cluster a > challenge as is. open up cqlsh, turn on tracing and try a few queries..how > many tombstones are scanned for a given query? It's possible the heap > problems you're seeing are actually happening on the query side and not on > the ingest side, the severity of this depends on driver and cassandra > version, but older drivers and versions of cassandra could easily overload > heap with expensive selects, when layered over tombstones it's certainly > becomes a possibility this is your root cause. > > Now this will primarily create more load on compaction and depending on > your cassandra version there maybe some other issue at work, but something > I can tell you is every time I see 1 dropped mutation I see a cluster that > was overloaded enough it had to shed load. If I see 200k I see a > cluster/configuration/hardware that is badly overloaded. > > I suggest the following > > - trace some of the queries used in prod > - monitor your ingest rate, see at what levels you run into issues > (GCInspector log messages, dropped mutations, etc) > - heap configuration we mentioned earlier..go ahead and monitor heap > usage, if it hits 75% repeated this is an indication of heavy load > - monitor dropped mutations..any dropped mutation is evidence of an > overloaded server, again the root cause can be many other problems that are > solvable with current hardware, and LOTS of people runs with nodes with > similar configuration. > > > On Tue, Dec 16, 2014 at 5:08 PM, Arne Claassen <a...@emotient.com> wrote: >> >> Not using any secondary indicies and memtable_flush_queue_size is the >> default 4. >> >> But let me tell you how data is "mutated" right now, maybe that will give >> you an insight on how this is happening >> >> Basically the frame data table has the following primary key: PRIMARY KEY >> ((id), trackid, "timestamp") >> >> Generally data is inserted once. So day to day writes are all new rows. >> However, when out process for generating analytics for these rows >> changes, we run the media back through again, causing overwrites. >> >> Up until last night, this was just a new insert because the PK never >> changed so it was always 1-to-1 overwrite of every row. >> >> Last night was the first time that a new change went in where the PK >> could actually change so now the process is always, DELETE by partition >> key, insert all rows for partition key, repeat. >> >> We two tables that have similar frame data projections and some other >> aggregates with much smaller row count per partition key. >> >> hope that helps, >> arne >> >> On Dec 16, 2014, at 2:46 PM, Ryan Svihla <rsvi...@datastax.com> wrote: >> >> so you've got some blocked flush writers but you have a incredibly large >> number of dropped mutations, are you using secondary indexes? and if so how >> many? what is your flush queue set to? >> >> On Tue, Dec 16, 2014 at 4:43 PM, Arne Claassen <a...@emotient.com> wrote: >>> >>> Of course QA decided to start a test batch (still relatively low >>> traffic), so I hope it doesn't throw the tpstats off too much >>> >>> Node 1: >>> Pool Name Active Pending Completed Blocked >>> All time blocked >>> MutationStage 0 0 13804928 0 >>> 0 >>> ReadStage 0 0 10975 0 >>> 0 >>> RequestResponseStage 0 0 7725378 0 >>> 0 >>> ReadRepairStage 0 0 1247 0 >>> 0 >>> ReplicateOnWriteStage 0 0 0 0 >>> 0 >>> MiscStage 0 0 0 0 >>> 0 >>> HintedHandoff 1 1 50 0 >>> 0 >>> FlushWriter 0 0 306 0 >>> 31 >>> MemoryMeter 0 0 719 0 >>> 0 >>> GossipStage 0 0 286505 0 >>> 0 >>> CacheCleanupExecutor 0 0 0 0 >>> 0 >>> InternalResponseStage 0 0 0 0 >>> 0 >>> CompactionExecutor 4 14 159 0 >>> 0 >>> ValidationExecutor 0 0 0 0 >>> 0 >>> MigrationStage 0 0 0 0 >>> 0 >>> commitlog_archiver 0 0 0 0 >>> 0 >>> AntiEntropyStage 0 0 0 0 >>> 0 >>> PendingRangeCalculator 0 0 11 0 >>> 0 >>> MemtablePostFlusher 0 0 1781 0 >>> 0 >>> >>> Message type Dropped >>> READ 0 >>> RANGE_SLICE 0 >>> _TRACE 0 >>> MUTATION 391041 >>> COUNTER_MUTATION 0 >>> BINARY 0 >>> REQUEST_RESPONSE 0 >>> PAGED_RANGE 0 >>> READ_REPAIR 0 >>> >>> Node 2: >>> Pool Name Active Pending Completed Blocked >>> All time blocked >>> MutationStage 0 0 997042 0 >>> 0 >>> ReadStage 0 0 2623 0 >>> 0 >>> RequestResponseStage 0 0 706650 0 >>> 0 >>> ReadRepairStage 0 0 275 0 >>> 0 >>> ReplicateOnWriteStage 0 0 0 0 >>> 0 >>> MiscStage 0 0 0 0 >>> 0 >>> HintedHandoff 2 2 12 0 >>> 0 >>> FlushWriter 0 0 37 0 >>> 4 >>> MemoryMeter 0 0 70 0 >>> 0 >>> GossipStage 0 0 14927 0 >>> 0 >>> CacheCleanupExecutor 0 0 0 0 >>> 0 >>> InternalResponseStage 0 0 0 0 >>> 0 >>> CompactionExecutor 4 7 94 0 >>> 0 >>> ValidationExecutor 0 0 0 0 >>> 0 >>> MigrationStage 0 0 0 0 >>> 0 >>> commitlog_archiver 0 0 0 0 >>> 0 >>> AntiEntropyStage 0 0 0 0 >>> 0 >>> PendingRangeCalculator 0 0 3 0 >>> 0 >>> MemtablePostFlusher 0 0 114 0 >>> 0 >>> >>> Message type Dropped >>> READ 0 >>> RANGE_SLICE 0 >>> _TRACE 0 >>> MUTATION 0 >>> COUNTER_MUTATION 0 >>> BINARY 0 >>> REQUEST_RESPONSE 0 >>> PAGED_RANGE 0 >>> READ_REPAIR 0 >>> >>> Node 3: >>> Pool Name Active Pending Completed Blocked >>> All time blocked >>> MutationStage 0 0 1539324 0 >>> 0 >>> ReadStage 0 0 2571 0 >>> 0 >>> RequestResponseStage 0 0 373300 0 >>> 0 >>> ReadRepairStage 0 0 325 0 >>> 0 >>> ReplicateOnWriteStage 0 0 0 0 >>> 0 >>> MiscStage 0 0 0 0 >>> 0 >>> HintedHandoff 1 1 21 0 >>> 0 >>> FlushWriter 0 0 38 0 >>> 5 >>> MemoryMeter 0 0 59 0 >>> 0 >>> GossipStage 0 0 21491 0 >>> 0 >>> CacheCleanupExecutor 0 0 0 0 >>> 0 >>> InternalResponseStage 0 0 0 0 >>> 0 >>> CompactionExecutor 4 9 85 0 >>> 0 >>> ValidationExecutor 0 0 0 0 >>> 0 >>> MigrationStage 0 0 0 0 >>> 0 >>> commitlog_archiver 0 0 0 0 >>> 0 >>> AntiEntropyStage 0 0 0 0 >>> 0 >>> PendingRangeCalculator 0 0 6 0 >>> 0 >>> MemtablePostFlusher 0 0 164 0 >>> 0 >>> >>> Message type Dropped >>> READ 0 >>> RANGE_SLICE 0 >>> _TRACE 0 >>> MUTATION 205259 >>> COUNTER_MUTATION 0 >>> BINARY 0 >>> REQUEST_RESPONSE 0 >>> PAGED_RANGE 0 >>> READ_REPAIR 18 >>> >>> >>> Compaction seems like the only thing consistently active and pending >>> >>> On Tue, Dec 16, 2014 at 2:18 PM, Ryan Svihla <rsvi...@datastax.com> >>> wrote: >>>> >>>> Ok based on those numbers I have a theory.. >>>> >>>> can you show me nodetool tptats for all 3 nodes? >>>> >>>> On Tue, Dec 16, 2014 at 4:04 PM, Arne Claassen <a...@emotient.com> >>>> wrote: >>>>> >>>>> No problem with the follow up questions. I'm on a crash course here >>>>> trying to understand what makes C* tick so I appreciate all feedback. >>>>> >>>>> We reprocessed all media (1200 partition keys) last night where >>>>> partition keys had somewhere between 4k and 200k "rows". After that >>>>> completed, no traffic went to cluster at all for ~8 hours and throughout >>>>> today, we may get a couple (less than 10) queries per second and maybe 3-4 >>>>> write batches per hour. >>>>> >>>>> I assume the last value in the Partition Size histogram is the largest >>>>> row: >>>>> >>>>> 20924300 bytes: 79 >>>>> 25109160 bytes: 57 >>>>> >>>>> The majority seems clustered around 200000 bytes. >>>>> >>>>> I will look at switching my inserts to unlogged batches since they are >>>>> always for one partition key. >>>>> >>>>> On Tue, Dec 16, 2014 at 1:47 PM, Ryan Svihla <rsvi...@datastax.com> >>>>> wrote: >>>>>> >>>>>> Can you define what is "virtual no traffic" sorry to be repetitive >>>>>> about that, but I've worked on a lot of clusters in the past year and >>>>>> people have wildly different ideas what that means. >>>>>> >>>>>> unlogged batches of the same partition key are definitely a >>>>>> performance optimization. Typically async is much faster and easier on >>>>>> the >>>>>> cluster when you're using multip partition key batches. >>>>>> >>>>>> nodetool cfhistograms <keyspace> <tablename> >>>>>> >>>>>> On Tue, Dec 16, 2014 at 3:42 PM, Arne Claassen <a...@emotient.com> >>>>>> wrote: >>>>>>> >>>>>>> Actually not sure why the machine was originally configured at 6GB >>>>>>> since we even started it on an r3.large with 15GB. >>>>>>> >>>>>>> Re: Batches >>>>>>> >>>>>>> Not using batches. I actually have that as a separate question on >>>>>>> the list. Currently I fan out async single inserts and I'm wondering if >>>>>>> batches are better since my data is inherently inserted in blocks of >>>>>>> ordered rows for a single partition key. >>>>>>> >>>>>>> >>>>>>> Re: Traffic >>>>>>> >>>>>>> There isn't all that much traffic. Inserts come in as blocks per >>>>>>> partition key, but then can be 5k-200k rows for that partition key. >>>>>>> Each of >>>>>>> these rows is less than 100k. It's small, lots of ordered rows. It's >>>>>>> frame >>>>>>> and sub-frame information for media. and rows for one piece of media is >>>>>>> inserted at once (the partition key). >>>>>>> >>>>>>> For the last 12 hours, where the load on all these machine has been >>>>>>> stuck there's been virtually no traffic at all. This is the nodes >>>>>>> basically >>>>>>> sitting idle, except that they had load of 4 each. >>>>>>> >>>>>>> BTW, how do you determine widest row or for that matter number of >>>>>>> tombstones in a row? >>>>>>> >>>>>>> thanks, >>>>>>> arne >>>>>>> >>>>>>> On Tue, Dec 16, 2014 at 1:24 PM, Ryan Svihla <rsvi...@datastax.com> >>>>>>> wrote: >>>>>>>> >>>>>>>> So 1024 is still a good 2.5 times what I'm suggesting, 6GB is >>>>>>>> hardly enough to run Cassandra well in, especially if you're going full >>>>>>>> bore on loads. However, you maybe just flat out be CPU bound on your >>>>>>>> write >>>>>>>> throughput, how many TPS and what size writes do you have? Also what is >>>>>>>> your widest row? >>>>>>>> >>>>>>>> Final question what is compaction throughput at? >>>>>>>> >>>>>>>> >>>>>>>> On Tue, Dec 16, 2014 at 3:20 PM, Arne Claassen <a...@emotient.com> >>>>>>>> wrote: >>>>>>>>> >>>>>>>>> The starting configuration I had, which is still running on two of >>>>>>>>> the nodes, was 6GB Heap, 1024MB parnew which is close to what you are >>>>>>>>> suggesting and those have been pegged at load 4 for the over 12 hours >>>>>>>>> with >>>>>>>>> hardly and read or write traffic. I will set one to 8GB/400MB and see >>>>>>>>> if >>>>>>>>> its load changes. >>>>>>>>> >>>>>>>>> On Tue, Dec 16, 2014 at 1:12 PM, Ryan Svihla <rsvi...@datastax.com >>>>>>>>> > wrote: >>>>>>>>> >>>>>>>>>> So heap of that size without some tuning will create a number of >>>>>>>>>> problems (high cpu usage one of them), I suggest either 8GB heap and >>>>>>>>>> 400mb >>>>>>>>>> parnew (which I'd only set that low for that low cpu count) , or >>>>>>>>>> attempt >>>>>>>>>> the tunings as indicated in >>>>>>>>>> https://issues.apache.org/jira/browse/CASSANDRA-8150 >>>>>>>>>> >>>>>>>>>> On Tue, Dec 16, 2014 at 3:06 PM, Arne Claassen <a...@emotient.com >>>>>>>>>> > wrote: >>>>>>>>>>> >>>>>>>>>>> Changed the 15GB node to 25GB heap and the nice CPU is down to >>>>>>>>>>> ~20% now. Checked my dev cluster to see if the ParNew log entries >>>>>>>>>>> are just >>>>>>>>>>> par for the course, but not seeing them there. However, both have >>>>>>>>>>> the >>>>>>>>>>> following every 30 seconds: >>>>>>>>>>> >>>>>>>>>>> DEBUG [BatchlogTasks:1] 2014-12-16 21:00:44,898 >>>>>>>>>>> BatchlogManager.java (line 165) Started replayAllFailedBatches >>>>>>>>>>> DEBUG [MemtablePostFlusher:1] 2014-12-16 21:00:44,899 >>>>>>>>>>> ColumnFamilyStore.java (line 866) forceFlush requested but >>>>>>>>>>> everything is >>>>>>>>>>> clean in batchlog >>>>>>>>>>> DEBUG [BatchlogTasks:1] 2014-12-16 21:00:44,899 >>>>>>>>>>> BatchlogManager.java (line 200) Finished replayAllFailedBatches >>>>>>>>>>> >>>>>>>>>>> Is that just routine scheduled house-keeping or a sign of >>>>>>>>>>> something else? >>>>>>>>>>> >>>>>>>>>>> On Tue, Dec 16, 2014 at 12:52 PM, Arne Claassen < >>>>>>>>>>> a...@emotient.com> wrote: >>>>>>>>>>>> >>>>>>>>>>>> Sorry, I meant 15GB heap on the one machine that has less nice >>>>>>>>>>>> CPU% now. The others are 6GB >>>>>>>>>>>> >>>>>>>>>>>> On Tue, Dec 16, 2014 at 12:50 PM, Arne Claassen < >>>>>>>>>>>> a...@emotient.com> wrote: >>>>>>>>>>>>> >>>>>>>>>>>>> AWS r3.xlarge, 30GB, but only using a Heap of 10GB, new 2GB >>>>>>>>>>>>> because we might go c3.2xlarge instead if CPU is more important >>>>>>>>>>>>> than RAM >>>>>>>>>>>>> Storage is optimized EBS SSD (but iostat shows no real IO >>>>>>>>>>>>> going on) >>>>>>>>>>>>> Each node only has about 10GB with ownership of 67%, 64.7% & >>>>>>>>>>>>> 68.3%. >>>>>>>>>>>>> >>>>>>>>>>>>> The node on which I set the Heap to 10GB from 6GB the >>>>>>>>>>>>> utlilization has dropped to 46%nice now, but the ParNew log >>>>>>>>>>>>> messages still >>>>>>>>>>>>> continue at the same pace. I'm gonna up the HEAP to 20GB for a >>>>>>>>>>>>> bit, see if >>>>>>>>>>>>> that brings that nice CPU further down. >>>>>>>>>>>>> >>>>>>>>>>>>> No TombstoneOverflowingExceptions. >>>>>>>>>>>>> >>>>>>>>>>>>> On Tue, Dec 16, 2014 at 11:50 AM, Ryan Svihla < >>>>>>>>>>>>> rsvi...@datastax.com> wrote: >>>>>>>>>>>>>> >>>>>>>>>>>>>> What's CPU, RAM, Storage layer, and data density per node? >>>>>>>>>>>>>> Exact heap settings would be nice. In the logs look for >>>>>>>>>>>>>> TombstoneOverflowingException >>>>>>>>>>>>>> >>>>>>>>>>>>>> >>>>>>>>>>>>>> On Tue, Dec 16, 2014 at 1:36 PM, Arne Claassen < >>>>>>>>>>>>>> a...@emotient.com> wrote: >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> I'm running 2.0.10. >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> The data is all time series data and as we change our >>>>>>>>>>>>>>> pipeline, we've been periodically been reprocessing the data >>>>>>>>>>>>>>> sources, which >>>>>>>>>>>>>>> causes each time series to be overwritten, i.e. every row per >>>>>>>>>>>>>>> partition key >>>>>>>>>>>>>>> is deleted and re-written, so I assume i've been collecting a >>>>>>>>>>>>>>> bunch of >>>>>>>>>>>>>>> tombstones. >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> Also, the presence of the ever present and never completing >>>>>>>>>>>>>>> compaction types, i assumed were an artifact of tombstoning, >>>>>>>>>>>>>>> but i fully >>>>>>>>>>>>>>> admit to conjecture based on about ~20 blog posts and >>>>>>>>>>>>>>> stackoverflow >>>>>>>>>>>>>>> questions i've surveyed. >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> I doubled the Heap on one node and it changed nothing >>>>>>>>>>>>>>> regarding the load or the ParNew log statements. New Generation >>>>>>>>>>>>>>> Usage is >>>>>>>>>>>>>>> 50%, Eden itself is 56%. >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> Anything else i should look at and report, let me know. >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> On Tue, Dec 16, 2014 at 11:14 AM, Jonathan Lacefield < >>>>>>>>>>>>>>> jlacefi...@datastax.com> wrote: >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> Hello, >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> What version of Cassandra are you running? >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> If it's 2.0, we recently experienced something similar >>>>>>>>>>>>>>>> with 8447 [1], which 8485 [2] should hopefully resolve. >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> Please note that 8447 is not related to tombstones. >>>>>>>>>>>>>>>> Tombstone processing can put a lot of pressure on the heap as >>>>>>>>>>>>>>>> well. Why do >>>>>>>>>>>>>>>> you think you have a lot of tombstones in that one particular >>>>>>>>>>>>>>>> table? >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> [1] https://issues.apache.org/jira/browse/CASSANDRA-8447 >>>>>>>>>>>>>>>> [2] https://issues.apache.org/jira/browse/CASSANDRA-8485 >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> Jonathan >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> [image: datastax_logo.png] >>>>>>>>>>>>>>>> Jonathan Lacefield >>>>>>>>>>>>>>>> Solution Architect | (404) 822 3487 | >>>>>>>>>>>>>>>> jlacefi...@datastax.com >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> [image: linkedin.png] >>>>>>>>>>>>>>>> <http://www.linkedin.com/in/jlacefield/> [image: >>>>>>>>>>>>>>>> facebook.png] <https://www.facebook.com/datastax> [image: >>>>>>>>>>>>>>>> twitter.png] <https://twitter.com/datastax> [image: g+.png] >>>>>>>>>>>>>>>> <https://plus.google.com/+Datastax/about> >>>>>>>>>>>>>>>> <http://feeds.feedburner.com/datastax> >>>>>>>>>>>>>>>> <https://github.com/datastax/> >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> On Tue, Dec 16, 2014 at 2:04 PM, Arne Claassen < >>>>>>>>>>>>>>>> a...@emotient.com> wrote: >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> I have a three node cluster that has been sitting at a >>>>>>>>>>>>>>>>> load of 4 (for each node), 100% CPI utilization (although 92% >>>>>>>>>>>>>>>>> nice) for >>>>>>>>>>>>>>>>> that last 12 hours, ever since some significant writes >>>>>>>>>>>>>>>>> finished. I'm trying >>>>>>>>>>>>>>>>> to determine what tuning I should be doing to get it out of >>>>>>>>>>>>>>>>> this state. The >>>>>>>>>>>>>>>>> debug log is just an endless series of: >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> DEBUG [ScheduledTasks:1] 2014-12-16 19:03:35,042 >>>>>>>>>>>>>>>>> GCInspector.java (line 118) GC for ParNew: 166 ms for 10 >>>>>>>>>>>>>>>>> collections, >>>>>>>>>>>>>>>>> 4400928736 used; max is 8000634880 >>>>>>>>>>>>>>>>> DEBUG [ScheduledTasks:1] 2014-12-16 19:03:36,043 >>>>>>>>>>>>>>>>> GCInspector.java (line 118) GC for ParNew: 165 ms for 10 >>>>>>>>>>>>>>>>> collections, >>>>>>>>>>>>>>>>> 4440011176 used; max is 8000634880 >>>>>>>>>>>>>>>>> DEBUG [ScheduledTasks:1] 2014-12-16 19:03:37,043 >>>>>>>>>>>>>>>>> GCInspector.java (line 118) GC for ParNew: 135 ms for 8 >>>>>>>>>>>>>>>>> collections, >>>>>>>>>>>>>>>>> 4402220568 used; max is 8000634880 >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> iostat shows virtually no I/O. >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> Compaction may enter into this, but i don't really know >>>>>>>>>>>>>>>>> what to make of compaction stats since they never change: >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> [root@cassandra-37919c3a ~]# nodetool compactionstats >>>>>>>>>>>>>>>>> pending tasks: 10 >>>>>>>>>>>>>>>>> compaction type keyspace table >>>>>>>>>>>>>>>>> completed total unit progress >>>>>>>>>>>>>>>>> Compaction mediamedia_tracks_raw >>>>>>>>>>>>>>>>> 271651482 563615497 bytes 48.20% >>>>>>>>>>>>>>>>> Compaction mediamedia_tracks_raw >>>>>>>>>>>>>>>>> 30308910 21676695677 bytes 0.14% >>>>>>>>>>>>>>>>> Compaction mediamedia_tracks_raw >>>>>>>>>>>>>>>>> 1198384080 1815603161 bytes 66.00% >>>>>>>>>>>>>>>>> Active compaction remaining time : 0h22m24s >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> 5 minutes later: >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> [root@cassandra-37919c3a ~]# nodetool compactionstats >>>>>>>>>>>>>>>>> pending tasks: 9 >>>>>>>>>>>>>>>>> compaction type keyspace table >>>>>>>>>>>>>>>>> completed total unit progress >>>>>>>>>>>>>>>>> Compaction mediamedia_tracks_raw >>>>>>>>>>>>>>>>> 271651482 563615497 bytes 48.20% >>>>>>>>>>>>>>>>> Compaction mediamedia_tracks_raw >>>>>>>>>>>>>>>>> 30308910 21676695677 bytes 0.14% >>>>>>>>>>>>>>>>> Compaction mediamedia_tracks_raw >>>>>>>>>>>>>>>>> 1198384080 1815603161 bytes 66.00% >>>>>>>>>>>>>>>>> Active compaction remaining time : 0h22m24s >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> Sure the pending tasks went down by one, but the rest is >>>>>>>>>>>>>>>>> identical. media_tracks_raw likely has a bunch of tombstones >>>>>>>>>>>>>>>>> (can't figure >>>>>>>>>>>>>>>>> out how to get stats on that). >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> Is this behavior something that indicates that i need more >>>>>>>>>>>>>>>>> Heap, larger new generation? Should I be manually running >>>>>>>>>>>>>>>>> compaction on >>>>>>>>>>>>>>>>> tables with lots of tombstones? >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> Any suggestions or places to educate myself better on >>>>>>>>>>>>>>>>> performance tuning would be appreciated. >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> arne >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> >>>>>>>>>>>>>> >>>>>>>>>>>>>> -- >>>>>>>>>>>>>> [image: datastax_logo.png] <http://www.datastax.com/> >>>>>>>>>>>>>> Ryan Svihla >>>>>>>>>>>>>> Solution Architect >>>>>>>>>>>>>> >>>>>>>>>>>>>> [image: twitter.png] <https://twitter.com/foundev> [image: >>>>>>>>>>>>>> linkedin.png] >>>>>>>>>>>>>> <http://www.linkedin.com/pub/ryan-svihla/12/621/727/> >>>>>>>>>>>>>> >>>>>>>>>>>>>> DataStax is the fastest, most scalable distributed database >>>>>>>>>>>>>> technology, delivering Apache Cassandra to the world’s most >>>>>>>>>>>>>> innovative >>>>>>>>>>>>>> enterprises. Datastax is built to be agile, always-on, and >>>>>>>>>>>>>> predictably >>>>>>>>>>>>>> scalable to any size. With more than 500 customers in 45 >>>>>>>>>>>>>> countries, DataStax >>>>>>>>>>>>>> is the database technology and transactional backbone of choice >>>>>>>>>>>>>> for the >>>>>>>>>>>>>> worlds most innovative companies such as Netflix, Adobe, Intuit, >>>>>>>>>>>>>> and eBay. >>>>>>>>>>>>>> >>>>>>>>>>>>>> >>>>>>>>>> >>>>>>>>>> -- >>>>>>>>>> [image: datastax_logo.png] <http://www.datastax.com/> >>>>>>>>>> Ryan Svihla >>>>>>>>>> Solution Architect >>>>>>>>>> >>>>>>>>>> [image: twitter.png] <https://twitter.com/foundev> [image: >>>>>>>>>> linkedin.png] >>>>>>>>>> <http://www.linkedin.com/pub/ryan-svihla/12/621/727/> >>>>>>>>>> >>>>>>>>>> DataStax is the fastest, most scalable distributed database >>>>>>>>>> technology, delivering Apache Cassandra to the world’s most >>>>>>>>>> innovative >>>>>>>>>> enterprises. Datastax is built to be agile, always-on, and >>>>>>>>>> predictably >>>>>>>>>> scalable to any size. With more than 500 customers in 45 countries, >>>>>>>>>> DataStax >>>>>>>>>> is the database technology and transactional backbone of choice for >>>>>>>>>> the >>>>>>>>>> worlds most innovative companies such as Netflix, Adobe, Intuit, and >>>>>>>>>> eBay. >>>>>>>>>> >>>>>>>>>> >>>>>>>> >>>>>>>> -- >>>>>>>> [image: datastax_logo.png] <http://www.datastax.com/> >>>>>>>> Ryan Svihla >>>>>>>> Solution Architect >>>>>>>> >>>>>>>> [image: twitter.png] <https://twitter.com/foundev> [image: >>>>>>>> linkedin.png] <http://www.linkedin.com/pub/ryan-svihla/12/621/727/> >>>>>>>> >>>>>>>> DataStax is the fastest, most scalable distributed database >>>>>>>> technology, delivering Apache Cassandra to the world’s most innovative >>>>>>>> enterprises. Datastax is built to be agile, always-on, and predictably >>>>>>>> scalable to any size. With more than 500 customers in 45 countries, >>>>>>>> DataStax >>>>>>>> is the database technology and transactional backbone of choice for the >>>>>>>> worlds most innovative companies such as Netflix, Adobe, Intuit, and >>>>>>>> eBay. >>>>>>>> >>>>>>>> >>>>>> >>>>>> -- >>>>>> [image: datastax_logo.png] <http://www.datastax.com/> >>>>>> Ryan Svihla >>>>>> Solution Architect >>>>>> >>>>>> [image: twitter.png] <https://twitter.com/foundev> [image: >>>>>> linkedin.png] <http://www.linkedin.com/pub/ryan-svihla/12/621/727/> >>>>>> >>>>>> DataStax is the fastest, most scalable distributed database >>>>>> technology, delivering Apache Cassandra to the world’s most innovative >>>>>> enterprises. Datastax is built to be agile, always-on, and predictably >>>>>> scalable to any size. With more than 500 customers in 45 countries, >>>>>> DataStax >>>>>> is the database technology and transactional backbone of choice for the >>>>>> worlds most innovative companies such as Netflix, Adobe, Intuit, and >>>>>> eBay. >>>>>> >>>>>> >>>> >>>> -- >>>> [image: datastax_logo.png] <http://www.datastax.com/> >>>> Ryan Svihla >>>> Solution Architect >>>> >>>> [image: twitter.png] <https://twitter.com/foundev> [image: >>>> linkedin.png] <http://www.linkedin.com/pub/ryan-svihla/12/621/727/> >>>> >>>> DataStax is the fastest, most scalable distributed database technology, >>>> delivering Apache Cassandra to the world’s most innovative enterprises. >>>> Datastax is built to be agile, always-on, and predictably scalable to any >>>> size. With more than 500 customers in 45 countries, DataStax is the >>>> database technology and transactional backbone of choice for the worlds >>>> most innovative companies such as Netflix, Adobe, Intuit, and eBay. >>>> >>>> >> >> -- >> [image: datastax_logo.png] <http://www.datastax.com/> >> Ryan Svihla >> Solution Architect >> >> [image: twitter.png] <https://twitter.com/foundev> [image: linkedin.png] >> <http://www.linkedin.com/pub/ryan-svihla/12/621/727/> >> >> DataStax is the fastest, most scalable distributed database technology, >> delivering Apache Cassandra to the world’s most innovative enterprises. >> Datastax is built to be agile, always-on, and predictably scalable to any >> size. With more than 500 customers in 45 countries, DataStax is the >> database technology and transactional backbone of choice for the worlds >> most innovative companies such as Netflix, Adobe, Intuit, and eBay. >> >> >> > > -- > [image: datastax_logo.png] <http://www.datastax.com/> > Ryan Svihla > Solution Architect > > [image: twitter.png] <https://twitter.com/foundev> [image: linkedin.png] > <http://www.linkedin.com/pub/ryan-svihla/12/621/727/> > > DataStax is the fastest, most scalable distributed database technology, > delivering Apache Cassandra to the world’s most innovative enterprises. > Datastax is built to be agile, always-on, and predictably scalable to any > size. With more than 500 customers in 45 countries, DataStax is the > database technology and transactional backbone of choice for the worlds > most innovative companies such as Netflix, Adobe, Intuit, and eBay. > > > -- [image: datastax_logo.png] <http://www.datastax.com/> Ryan Svihla Solution Architect [image: twitter.png] <https://twitter.com/foundev> [image: linkedin.png] <http://www.linkedin.com/pub/ryan-svihla/12/621/727/> DataStax is the fastest, most scalable distributed database technology, delivering Apache Cassandra to the world’s most innovative enterprises. Datastax is built to be agile, always-on, and predictably scalable to any size. With more than 500 customers in 45 countries, DataStax is the database technology and transactional backbone of choice for the worlds most innovative companies such as Netflix, Adobe, Intuit, and eBay.
