write throughput is significantly slower when repository grows.
Below is repo intilization code snippet
FileStore fs = FileStoreBuilder.fileStoreBuilder(new File(repoConfig.homeDir))
.withIOLogging(LoggerFactory.getLogger("IOLog")).withSegmentCacheSize(256)
.withMemoryMapping(true).withStrictVersionCheck(false).build();
SegmentNodeStore ns = SegmentNodeStoreBuilders.builder(fs).build();
SecurityProvider sp = createSecurityProvider();
Oak oak = new Oak(ns);
Repository repository = new Jcr(oak.with(sp).withAtomicCounter()).createRepository();
oak.version :1.38.0
java 8
we are creating session(repository.login(cred)) and logging out session(session.logout()) for each save
We have 3 million files to migrate and after 100000 files performance got degraded and taking around 48 hours to complete 0.5 million files .
From Thread dump oak-scheduled-executor 32 threads are in TIMED_WAITING (parking) state.
Related
I have a large database (100M rows) indexed by SphinxSearch. Each search takes 0.1-0.5s. However, if I run 10 searches concurrently, they take 20s on average.
Is it the expected behaviour of SphinxSearch?
Should I adjust the config or move to another search engine for concurrency?
My config file is simple:
searchd
{
listen = 9312
listen = 9306:mysql41
pid_file = /var/searchd.pid
read_timeout = 30
log = /var/log/sphinxsearch/searchd.log
query_log = /var/log/sphinxsearch/query.log
}
Is it the expected behaviour of SphinxSearch?
It heavily depends on the number of CPUs. If you have more than 10 physical CPUs then latency degradation from 0.5 sec to 20 sec by increasing the concurrency from 1 to 10 is definitely not expected. In this case first of all make sure all your CPUs are busy under the concurrency load. If it's not - depending on your Sphinx version and multi-tasking mode let it run with more threads.
Should I adjust the config or move to another search engine for concurrency?
I recommend Manticore Search as:
it's open source - https://github.com/manticoresoftware/manticoresearch/
it's the only fork of Sphinx and if you are familiar with Sphinx in general it shouldn't be a problem to migrate
hundreds of bugs have been fixed
the multi-tasking mode is completely different (coroutines)
I need to copy existing private s3 files to another directory but my process is too slow, on my local its 2 seconds per file_get_content of each file.
My problem is most files that I process are 50+ files so if you total that would be 2seconds * 50 and its really not a great user experience waiting for that amount of time for a process to finish, what might be the best approach I can do to refactor this? queue is not really an option at the moment
foreach ($sourceAttachmentFiles as $sourceAttachmentFile) {
$newFullFileName = $newDirectory.$sourceAttachmentFile->filename;
// 2 seconds every loop
$content = file_get_contents($sourceAttachmentFile->getS3SignedUrl());
Storage::disk('s3')->put($newFullFileName, $content, 'private');
}
Spring boot project, log how many time took to save 2 DB,
long start = System.currentTimeMillis();
getDao().batchInsert(batchList);
long end = System.currentTimeMillis();
log.info("Save {} data 2 DB successfully took time: {}", getDescName(), (end - start));
Very strangely, I found there is situation time cost is negative，see below
2019-05-16 14:41:04.420 INFO 3324 --- [ave2db-thread-2] c.c.sz_vss.demo.AbstractSave2DBProcess : Save Stock data 2 DB batch size: 416
2019-05-16 14:41:03.152 INFO 3324 --- [ave2db-thread-2] c.c.sz_vss.demo.AbstractSave2DBProcess : Save Stock data 2 DB successfully took time: -1268
Why does this happen? Is it spring boot log system bug? or is it the Windows system time could retreat occasionally?
Network time synchronization can apply a correction in either direction, so yes the system calendar can move backwards. Also in timezones that observe daylight savings time, you will see +/- 1 hour discontinuities each year.
That's why it is not recommended to use the system calendar for measuring elapsed time. There are monotonic timers (on Windows, QueryPerformanceCounter() combined with QueryPerformanceFrequency(), on POSIX such as Linux it is clock_gettime(CLOCK_MONOTONIC)). Managed frameworks usually wrap these in an object named "Stopwatch".
I am using Grafana version 5.1.3 (commit: 087143285) ,InfluxDB shell version: 1.5.2 along with jmeter.
There are 13 panels. Panel is taking 5 to 8 seconds to load.
Below query is running for panel:(When I run the same query on db server it is running very fast )
SELECT mean(“startedThreads”) FROM “virtualUsers” WHERE time >= 1537865329564ms and time <= 1537867129564ms GROUP BY time(60s) fill(null);
EXPLAIN ANALYZE
execution_time: 157.341µs
planning_time: 626.44µs
total_time: 783.781µs
SELECT count(“responseTime”)/60 FROM “requestsRaw” WHERE time >= 1537865329564ms and time <= 1537867129564ms GROUP BY time(60s) fill(null)"
execution_time: 535.011µs
planning_time: 1.805892ms
total_time: 2.340903ms
Below is memory and cpu details.Influx db and Grafans are hosted on same server.
free -g
total used free shared buff/cache available
Mem: 15 3 11 0 1 12
Swap: 7 0 6
CPU(s): 2
On-line CPU(s) list: 0,1
Thread(s) per core: 1
Core(s) per socket: 2
Socket(s): 1
And as per my initial understanding Grafana minimum memory requirement is 249MB.So memory is not problem for Grafana.
Please let me if you need more details.
It is odd that the query runs fast while Grafana needs a long time. Panels should be diplayed as soon as Grafana gets a response.
Since rendering is done in the Browser AFAIK this could be the bottleneck. So if your Browser runs on a Raspberry Pi 1, please try using a different computer.
It is not clear if all Panels need a long time to load or if it is just one Panel that needs a long time. You should try to find out if the loading time is related to just one Panel.
Lastly consider that all queries are sent at the same time, so making just one query to the server from CLI may not be representative. You could try to spliting the Dashboard in multiple Dashboards to improve the loading time.
I am trying to query from s3 (15 days of data). I tried querying them separately (each day) it works fine. It works fine for 14 days as well. But when I query 15 days the job keeps running forever (hangs) and the task # is not updating.
My settings :
I am using 51 node cluster r3.4x large with dynamic allocation and maximum resource turned on.
All I am doing is =
val startTime="2017-11-21T08:00:00Z"
val endTime="2017-12-05T08:00:00Z"
val start = DateUtils.getLocalTimeStamp( startTime )
val end = DateUtils.getLocalTimeStamp( endTime )
val days: Int = Days.daysBetween( start, end ).getDays
val files: Seq[String] = (0 to days)
.map( start.plusDays )
.map( d => s"$input_path${DateTimeFormat.forPattern( "yyyy/MM/dd" ).print( d )}/*/*" )
sqlSession.sparkContext.textFile( files.mkString( "," ) ).count
When I run the same with 14 days, I got 197337380 (count) and I ran the 15th day separately and got 27676788. But when I query 15 days total the job hangs
Update :
The job works fine with :
var df = sqlSession.createDataFrame(sc.emptyRDD[Row], schema)
for(n <- files ){
val tempDF = sqlSession.read.schema( schema ).json(n)
df = df(tempDF)
}
df.count
But can some one explain why it works now but not before ?
UPDATE : After setting mapreduce.input.fileinputformat.split.minsize to 256 GB it works fine now.
Dynamic allocation and maximize resource allocation are both different settings, one would be disabled when other is active. With Maximize resource allocation in EMR, 1 executor per node is launched, and it allocates all the cores and memory to that executor.
I would recommend taking a different route. You seem to have a pretty big cluster with 51 nodes, not sure if it is even required. However, follow this rule of thumb to begin with, and you will get a hang of how to tune these configurations.
Cluster memory - minimum of 2X the data you are dealing with.
Now assuming 51 nodes is what you require, try below:
r3.4x has 16 CPUs - so you can put all of them to use by leaving one for the OS and other processes.
Set your number of executors to 150 - this will allocate 3 executors per node.
Set number of cores per executor to 5 (3 executors per node)
Set your executor memory to roughly total host memory/3 = 35G
You got to control the parallelism (default partitions), set this to number of total cores you have ~ 800
Adjust shuffle partitions - make this twice of number of cores - 1600
Above configurations have been working like a charm for me. You can monitor the resource utilization on Spark UI.
Also, in your yarn config /etc/hadoop/conf/capacity-scheduler.xml file, set yarn.scheduler.capacity.resource-calculator to org.apache.hadoop.yarn.util.resource.DominantResourceCalculator - which will allow Spark to really go full throttle with those CPUs. Restart yarn service after change.
You should be increasing the executor memory and # executors, If the data is huge try increasing the Driver memory.
My suggestion is to not use the dynamic resource allocation and let it run and see if it still hangs or not (Please note that spark job can consume entire cluster resources and make other applications starve for resources try this approach when no jobs are running). if it doesn't hang that means you should play with the resource allocation, then start hardcoding the resources and keep increasing resources so that you can find the best resource allocation you can possibly use.
Below links can help you understand the resource allocation and optimization of resources.
http://site.clairvoyantsoft.com/understanding-resource-allocation-configurations-spark-application/
https://community.hortonworks.com/articles/42803/spark-on-yarn-executor-resource-allocation-optimiz.html