Hi all, I ran a few experiments in the last days trying to identify what is the bottleneck for the ingestion process. - Running 10 tservers per node instead of only one gave me a very neglectable performance improvement of about 15%. - Running the ingestor processes from the two masters give the same performance as running one ingestor process in each tablet server (10 ingestors) - neither the network limit (10 Gb network) nor the disk throughput limit has been reached (1GB/s per node reached while running the TestDFSIO benchmark on HDFS) - CPU is always around 20% on each tserver - changing compression from GZ to snappy did not provide any benefit - increasing the tserver.total.mutation.queue.max to 200MB actually decreased the performance I am going to run some ingestion experiment with Kudu over the next few days, but any other suggestion on how improve the performance on Accumulo is very welcome. Thanks.
Best Regards, Massimiliano From: Jonathan Wonders <[email protected]> To: [email protected], Dave Marion <[email protected]> Date: 07/07/2017 04:02 Subject: Re: maximize usage of cluster resources during ingestion I've personally never seen full CPU utilization during pure ingest. Typically the bottleneck has been I/O related. The majority of steady-state CPU utilization under a heavy ingest load is probably due to compression unless you have custom constraints running. This can depend on the compression algorithm you have selected. There is probably a measurable contribution from inserting into the in-memory map. Otherwise, not much computation occurs during ingest per mutation. On Thu, Jul 6, 2017 at 8:18 AM, Dave Marion <[email protected]> wrote: That's a good point. I would also look at increasing tserver.total.mutation.queue.max. Are you seeing hold times? If not, I would keep pushing harder until you do, then move to multiple tablet servers. Do you have any GC logs? On July 6, 2017 at 4:47 AM Cyrille Savelief <[email protected]> wrote: Are you sure Accumulo is not waiting for your app's data? There might be GC pauses in your ingest code (we have already experienced that). Le jeu. 6 juil. 2017 à 10:32, Massimilian Mattetti <[email protected]> a écrit : Thank you all for the suggestions. About the native memory map I checked the logs on each tablet server and it was loaded correctly (of course the tserver.memory.maps.native.enabled was set to true), so the GC pauses should not be the problem eventually. I managed to get much better ingestion graph by reducing the native map size to 2GB and increasing the Batch Writer threads number from the default (3 was really bad for my configuration) to 10 (I think it does not make sense having more threads than tablet servers, am I right?). The configuration that I used for the table is: "table.file.replication": "2", "table.compaction.minor.logs.threshold": "3", "table.durability": "flush", "table.split.threshold": "1G" while for the tablet servers is: "tserver.wal.blocksize": "1G", "tserver.walog.max.size": "2G", "tserver.memory.maps.max": "2G", "tserver.compaction.minor.concurrent.max": "50", "tserver.compaction.major.concurrent.max": "20", "tserver.wal.replication": "2", "tserver.compaction.major.thread.files.open.max": "15" The new graph: I still have the problem of a CPU usage that is less than 20%. So I am thinking to run multiple tablet servers per node (like 5 or 10) in order to maximize the CPU usage. Besides that I do not have any other idea on how to stress those servers with ingestion. Any suggestions are very welcome. Meanwhile, thank you all again for your help. Best Regards, Massimiliano From: Jonathan Wonders <[email protected]> To: [email protected] Date: 06/07/2017 04:01 Subject: Re: maximize usage of cluster resources during ingestion Hi Massimilian, Are you seeing held commits during the ingest pauses? Just based on having looked at many similar graphs in the past, this might be one of the major culprits. A tablet server has a memory region with a bounded size (tserver.memory.maps.max) where it buffers data that has not yet been written to RFiles (through the process of minor compaction). The region is segmented by tablet and each tablet can have a buffer that is undergoing ingest as well as a buffer that is undergoing minor compaction. A memory manager decides when to initiate minor compactions for the tablet buffers and the default implementation tries to keep the memory region 80-90% full while preferring to compact the largest tablet buffers. Creating larger RFiles during minor compaction should lead to less major compactions. During a minor compaction, the tablet buffer still "consumes" memory within the in memory map and high ingest rates can lead to exhausing the remaining capacity. The default memory manage uses an adaptive strategy to predict the expected memory usage and makes compaction decisions that should maintain some free memory. Batch writers can be bursty and a bit unpredictable which could throw off these estimates. Also, depending on the ingest profile, sometimes an in-memory tablet buffer will consume a large percentage of the total buffer. This leads to long minor compactions when the buffer size is large which can allow ingest enough time to exhaust the buffer before that memory can be reclaimed. When a tablet server has to block ingest, it can affect client ingest rates to other tablet servers due to the way that batch writers work. This can lead to other tablet servers underestimating future ingest rates which can further exacerbate the problem. There are some configuration changes that could reduce the severity of held commits, although they might reduce peak ingest rates. Reducing the in memory map size can reduce the maximum pause time due to held commits. Adding additional tablets should help avoid the problem of a single tablet buffer consuming a large percentage of the memory region. It might be better to aim for ~20 tablets per server if your problem allows for it. It is also possible to replace the memory manager with a custom one. I've tried this in the past and have seen stability improvements by making the memory thresholds less aggressive (50-75% full). This did reduce peak ingest rate in some cases, but that was a reasonable tradeoff. Based on your current configuration, if a tablet server is serving 4 tablets and has a 32GB buffer, your first minor compactions will be at least 8GB and they will probably grow larger over time until the tablets naturally split. Consider how long it would take to write this RFile compared to your peak ingest rate. As others have suggested, make sure to use the native maps. Based on your current JVM heap size, using the Java in-memory map would probably lead to OOME or very bad GC performance. Accumulo can trace minor compaction durations so you can get a feel for max pause times or measure the effect of configuration changes. Cheers, --Jonathan On Wed, Jul 5, 2017 at 7:16 PM, Dave Marion <[email protected]> wrote: Based on what Cyrille said, I would look at garbage collection, specifically I would look at how much of your newly allocated objects spill into the old generation before they are flushed to disk. Additionally, I would turn off the debug log or log to SSD?s if you have them. Another thought, seeing that you have 256GB RAM / node, is to run multiple tablet servers per node. Do you have 10 threads on your Batch Writers? What about the Batch Writer latency, is it too low such that you are not filling the buffer? From: Massimilian Mattetti [mailto:[email protected]] Sent: Wednesday, July 05, 2017 8:37 AM To: [email protected] Subject: maximize usage of cluster resources during ingestion Hi all, I have an Accumulo 1.8.1 cluster made by 12 bare metal servers. Each server has 256GB of Ram and 2 x 10 cores CPU. 2 machines are used as masters (running HDFS NameNodes, Accumulo Master and Monitor). The other 10 machines has 12 Disks of 1 TB (11 used by HDFS DataNode process) and are running Accumulo TServer processes. All the machines are connected via a 10Gb network and 3 of them are running ZooKeeper. I have run some heavy ingestion test on this cluster but I have never been able to reach more than 20% CPU usage on each Tablet Server. I am running an ingestion process (using batch writers) on each data node. The table is pre-split in order to have 4 tablets per tablet server. Monitoring the network I have seen that data is received/sent from each node with a peak rate of about 120MB/s / 100MB/s while the aggregated disk write throughput on each tablet servers is around 120MB/s. The table configuration I am playing with are: "table.file.replication": "2", "table.compaction.minor.logs.threshold": "10", "table.durability": "flush", "table.file.max": "30", "table.compaction.major.ratio": "9", "table.split.threshold": "1G" while the tablet server configuration is: "tserver.wal.blocksize": "2G", "tserver.walog.max.size": "8G", "tserver.memory.maps.max": "32G", "tserver.compaction.minor.concurrent.max": "50", "tserver.compaction.major.concurrent.max": "8", "tserver.total.mutation.queue.max": "50M", "tserver.wal.replication": "2", "tserver.compaction.major.thread.files.open.max": "15" the tablet server heap has been set to 32GB >From Monitor UI As you can see I have a lot of valleys in which the ingestion rate reaches 0. What would be a good procedure to identify the bottleneck which causes the 0 ingestion rate periods? Thanks. Best Regards, Max
