I have a 2 node test cluster on AWS with spark-2.0.0-bin-hadoop2.7 installed.
This is the code I'm using to launch the cluster.
./spark-ec2 -k blah -i blah.pem -r us-west-1 -s 1 -t r3.2xlarge launch --copy-aws-credentials blah
Viewing port 8080 shows 58.8GB(0.0 B Used) of memory after running these two lines in rstudio.
Sys.setenv(SPARK_HOME="/root/spark")
library(SparkR, lib.loc = c(file.path(Sys.getenv("SPARK_HOME"), "R", "lib")))
When I run this line and refresh the page on port 8080 the memory usage changes to 58.8 GB (53.8 GB Used).
sparkR.session(master = "spark://[ip]:7077",
sparkHome = '/root/spark',
enableHiveSupport = FALSE)
When I try to create a spark data frame from a data frame which should consume 0.04857268 GB of memory I get this error:
acquisition <- as.DataFrame(orig)
17/11/04 14:27:23 WARN TaskSetManager: Stage 0 contains a task of very large size (166360 KB). The maximum recommended task size is 100 KB.
Exception in thread "dispatcher-event-loop-1" java.lang.OutOfMemoryError: Java heap space
I tried adding this but get the same error.
options(java.parameters = "-Xmx2048m")
install.packages("rJava")
library(rJava)
I'm stuck. I've spent three weekends googling this issue and can't figure it out.
Thanks.
Related
I'm working with OpenJDK 11 and a very simple SpringBoot application that almost the only thing it has is the SpringBoot actuator enabled so I can call /actuator/health etc.
I also have a kubernetes cluster on GCE very simple with just a pod with a container (containing this app of course)
My configuration has some key points that I want to highlight, it has some requirements and limits
resources:
limits:
memory: 600Mi
requests:
memory: 128Mi
And it has a readiness probe
readinessProbe:
initialDelaySeconds: 30
periodSeconds: 30
httpGet:
path: /actuator/health
port: 8080
I'm also setting a JVM_OPTS like (that my program is using obviously)
env:
- name: JVM_OPTS
value: "-XX:MaxRAM=512m"
The problem
I launch this and it gets OOMKilled in about 3 hours every time!
I'm never calling anything myself the only call is the readiness probe each 30 seconds that kubernetes does, and that is enough to exhaust the memory ? I have also not implemented anything out of the ordinary, just a Get method that says hello world along all the SpringBoot imports to have the actuators
If I run kubectl top pod XXXXXX I actually see how gradually get bigger and bigger
I have tried a lot of different configurations, tips, etc, but anything seems to work with a basic SpringBoot app
Is there a way to actually hard limit the memory in a way that Java can raise a OutOfMemory exception ? or to prevent this from happening?
Thanks in advance
EDIT: After 15h running
NAME READY STATUS RESTARTS AGE
pod/test-79fd5c5b59-56654 1/1 Running 4 15h
describe pod says...
State: Running
Started: Wed, 27 Feb 2019 10:29:09 +0000
Last State: Terminated
Reason: OOMKilled
Exit Code: 137
Started: Wed, 27 Feb 2019 06:27:39 +0000
Finished: Wed, 27 Feb 2019 10:29:08 +0000
That last span of time is about 4 hours and only have 483 calls to /actuator/health, apparently that was enough to make java exceed the MaxRAM hint ?
EDIT: Almost 17h
its about to die again
$ kubectl top pod test-79fd5c5b59-56654
NAME CPU(cores) MEMORY(bytes)
test-79fd5c5b59-56654 43m 575Mi
EDIT: loosing any hope at 23h
NAME READY STATUS RESTARTS AGE
pod/test-79fd5c5b59-56654 1/1 Running 6 23h
describe pod:
State: Running
Started: Wed, 27 Feb 2019 18:01:45 +0000
Last State: Terminated
Reason: OOMKilled
Exit Code: 137
Started: Wed, 27 Feb 2019 14:12:09 +0000
Finished: Wed, 27 Feb 2019 18:01:44 +0000
EDIT: A new finding
Yesterday night I was doing some interesting reading:
https://developers.redhat.com/blog/2017/03/14/java-inside-docker/
https://banzaicloud.com/blog/java10-container-sizing/
https://medium.com/adorsys/jvm-memory-settings-in-a-container-environment-64b0840e1d9e
TL;DR I decided to remove the memory limit and start the process again, the result was quite interesting (after like 11 hours running)
NAME CPU(cores) MEMORY(bytes)
test-84ff9d9bd9-77xmh 218m 1122Mi
So... WTH with that CPU? I kind expecting a big number on memory usage but what happens with the CPU?
The one thing I can think is that the GC is running as crazy thinking that the MaxRAM is 512m and he is using more than 1G. I'm wondering, is Java detecting ergonomics correctly? (I'm starting to doubt it)
To test my theory I set a limit of 512m and deploy the app this way and I found that from the start there is a unusual CPU load that it has to be the GC running very frequently
kubectl create ...
limitrange/mem-limit-range created
pod/test created
kubectl exec -it test-64ccb87fd7-5ltb6 /usr/bin/free
total used free shared buff/cache available
Mem: 7658200 1141412 4132708 19948 2384080 6202496
Swap: 0 0 0
kubectl top pod ..
NAME CPU(cores) MEMORY(bytes)
test-64ccb87fd7-5ltb6 522m 283Mi
522m is too much vCPU, so my logical next step was to ensure I'm using the most appropriated GC for this case, I changed the JVM_OPTS this way:
env:
- name: JVM_OPTS
value: "-XX:MaxRAM=512m -Xmx128m -XX:+UseSerialGC"
...
resources:
requests:
memory: 256Mi
cpu: 0.15
limits:
memory: 700Mi
And thats bring the vCPU usage to a reasonable status again, after kubectl top pod
NAME CPU(cores) MEMORY(bytes)
test-84f4c7445f-kzvd5 13m 305Mi
Messing with Xmx having MaxRAM is obviously affecting the JVM but how is not possible to control the amount of memory we have on virtualized containers ? I know that free command will report the host available RAM but OpenJDK should be using cgroups rihgt?.
I'm still monitoring the memory ...
EDIT: A new hope
I did two things, the first one was to remove again my container limit, I want to analyze how much it will grow, also I added a new flag to see how the process is using the native memory -XX:NativeMemoryTracking=summary
At the beginning every thing was normal, the process started consuming like 300MB via kubectl top pod so I let it running about 4 hours and then ...
kubectl top pod
NAME CPU(cores) MEMORY(bytes)
test-646864bc48-69wm2 54m 645Mi
kind of expected, right ? but then I checked the native memory usage
jcmd <PID> VM.native_memory summary
Native Memory Tracking:
Total: reserved=2780631KB, committed=536883KB
- Java Heap (reserved=131072KB, committed=120896KB)
(mmap: reserved=131072KB, committed=120896KB)
- Class (reserved=203583KB, committed=92263KB)
(classes #17086)
( instance classes #15957, array classes #1129)
(malloc=2879KB #44797)
(mmap: reserved=200704KB, committed=89384KB)
( Metadata: )
( reserved=77824KB, committed=77480KB)
( used=76069KB)
( free=1411KB)
( waste=0KB =0.00%)
( Class space:)
( reserved=122880KB, committed=11904KB)
( used=10967KB)
( free=937KB)
( waste=0KB =0.00%)
- Thread (reserved=2126472KB, committed=222584KB)
(thread #2059)
(stack: reserved=2116644KB, committed=212756KB)
(malloc=7415KB #10299)
(arena=2413KB #4116)
- Code (reserved=249957KB, committed=31621KB)
(malloc=2269KB #9949)
(mmap: reserved=247688KB, committed=29352KB)
- GC (reserved=951KB, committed=923KB)
(malloc=519KB #1742)
(mmap: reserved=432KB, committed=404KB)
- Compiler (reserved=1913KB, committed=1913KB)
(malloc=1783KB #1343)
(arena=131KB #5)
- Internal (reserved=7798KB, committed=7798KB)
(malloc=7758KB #28415)
(mmap: reserved=40KB, committed=40KB)
- Other (reserved=32304KB, committed=32304KB)
(malloc=32304KB #3030)
- Symbol (reserved=20616KB, committed=20616KB)
(malloc=17475KB #212850)
(arena=3141KB #1)
- Native Memory Tracking (reserved=5417KB, committed=5417KB)
(malloc=347KB #4494)
(tracking overhead=5070KB)
- Arena Chunk (reserved=241KB, committed=241KB)
(malloc=241KB)
- Logging (reserved=4KB, committed=4KB)
(malloc=4KB #184)
- Arguments (reserved=17KB, committed=17KB)
(malloc=17KB #469)
- Module (reserved=286KB, committed=286KB)
(malloc=286KB #2704)
Wait, What ? 2.1 GB reserved for threads? and 222 MB being used, what is this ? I currently don't know, I just saw it...
I need time trying to understand why this is happening
I finally found my issue and I want to share it so others can benefit in some way from this.
As I found on my last edit I had a thread problem that was causing all the memory consumption over time, specifically we was using an asynchronous method from a third party library without properly taking care those resources (ensure those calls was ending correctly in this case).
I was able to detect the issue because I used a memory limit on my kubernete deployment from the beginning (which is a good practice on production environments) and then I monitored very closely my app memory consumption using tools like jstat, jcmd, visualvm, kill -3 and most importantly the -XX:NativeMemoryTracking=summary flag that gave me so much detail in this regard.
I am running spark job on hadoop cluster, and the job is failing at few times with the exception :
exception : Failing Oozie Launcher, Main class [org.apache.oozie.action.hadoop.JavaMain], main() threw exception, begin > end in range (begin, end): (1494159709088, 1494159706071)
the job ran successfully on the rerun.
After searching on google, It might be Clock skew between the Oozie server host and launcher host.
Is there a way i can check if there is clock skew ? or how can i check the time on all the nodes whether they are in sync or not.
Thanks
ntptime command output :
ntp_gettime() returns code 0 (OK)
time dcb9b19b.a2328f64 Sun, May 7 2017 14:45:47.633, (.633584090),
maximum error 434990 us, estimated error 815 us, TAI offset 0
ntp_adjtime() returns code 0 (OK)
modes 0x0 (),
offset 176.871 us, frequency -25.666 ppm, interval 1 s,
maximum error 434990 us, estimated error 815 us,
status 0x2001 (PLL,NANO),
time constant 10, precision 0.001 us, tolerance 500 ppm,
ntpstat command output :
synchronised to NTP server (174.68.168.57) at stratum 3
time correct to within 77 ms
polling server every 1024 s
I am having a problem with tomcat since switching to a different package provider (bitnami -> official debian).
Someone seems to be hitting our servers with a request (with malicious intent):
59.111.29.6 - - [04/Feb/2017:16:17:58 +0000] "-" 400 -
where "-" is the request path, which coincides with
Feb 04, 2017 4:17:58 PM org.apache.coyote.http11.AbstractHttp11Processor process
INFO: Error parsing HTTP request header
Note: further occurrences of HTTP header parsing errors will be logged at DEBUG level.
which coincides with the increased CPU usage.
The server status shows the following:
<h1>JVM</h1><p> Free memory: 355.58 MB Total memory: 833.13 MB Max memory: 2900.00 MB</p><table border="0"><thead><tr><th>Memory Pool</th><th>Type</th><th>Initial</th><th>Total</th><th>Maximum</th><th>Used</th></tr></thead><tbody><tr><td>Eden Space</td><td>Heap memory</td><td>34.12 MB</td><td>229.93 MB</td><td>800.00 MB</td><td>12.47 MB (1%)</td></tr><tr><td>Survivor Space</td><td>Heap memory</td><td>4.25 MB</td><td>28.68 MB</td><td>100.00 MB</td><td>2.22 MB (2%)</td></tr><tr><td>Tenured Gen</td><td>Heap memory</td><td>85.37 MB</td><td>574.51 MB</td><td>2000.00 MB</td><td>462.84 MB (23%)</td></tr><tr><td>Code Cache</td><td>Non-heap memory</td><td>2.43 MB</td><td>7.00 MB</td><td>48.00 MB</td><td>6.89 MB (14%)</td></tr><tr><td>Perm Gen</td><td>Non-heap memory</td><td>128.00 MB</td><td>128.00 MB</td><td>512.00 MB</td><td>52.57 MB (10%)</td></tr></tbody></table><h1>"http-nio-8080"</h1><p> Max threads: 200 Current thread count: 10 Current thread busy: 3 Keeped alive sockets count: 1<br> Max processing time: 301 ms Processing time: 71.068 s Request count: 10021 Error count: 2996 Bytes received: 0.00 MB Bytes sent: 3.18 MB</p><table border="0"><tr><th>Stage</th><th>Time</th><th>B Sent</th><th>B Recv</th><th>Client (Forwarded)</th><th>Client (Actual)</th><th>VHost</th><th>Request</th></tr><tr><td><strong>F</strong></td><td>1486364749526 ms</td><td>0 KB</td><td>0 KB</td><td>185.40.4.169</td><td>185.40.4.169</td><td nowrap>?</td><td nowrap class="row-left">? ? ?</td></tr><tr><td><strong>F</strong></td><td>1486364749526 ms</td><td>0 KB</td><td>0 KB</td><td>185.40.4.169</td><td>185.40.4.169</td><td nowrap>?</td><td nowrap class="row-left">? ? ?</td></tr><tr><td><strong>R</strong></td><td>?</td><td>?</td><td>?</td><td>?</td><td>?</td><td>?</td></tr><tr><td><strong>S</strong></td><td>36 ms</td><td>0 KB</td><td>0 KB</td><td>106.51.39.130</td><td>106.51.39.130</td><td nowrap>104.197.119.177</td><td nowrap class="row-left">GET /manager/status?org.apache.catalina.filters.CSRF_NONCE=072F9F6884D94C5D7B30D1D34CE61BD9 HTTP/1.1</td></tr><tr><td><strong>R</strong></td><td>?</td><td>?</td><td>?</td><td>?</td><td>?</td><td>?</td></tr></table><p>P: Parse and prepare request S: Service F: Finishing R: Ready K: Keepalive</p><hr size="1" noshade="noshade">
<center><font size="-1" color="#525D76">
So it doesn't seem like an out of memory issue (but I could be wrong).
How can I stop someone from making the request in the first place to avoid the issues I'm facing? My webapp running on tomcat restricts HTTP methods to GET/POST, but how can I configure tomcat as a whole to restrict them?
I would advise you to obtain a thread dump of your server :
Isolates the PID of the tomcat server using :
jps -l
Obtains a thread dump using :
kill -3 PID
or jstack PID
Then checks the Thread dump, you should find the reason of the hogging thread
I'm running a very simple pig script (pig 0.14, Hadoop 2.4) :
customers = load '/some/hdfs/path' using SomeUDFLoader();
customers2 = foreach (group customers by customer_id) generate FLATTEN(group) as customer_id, MIN(dw_customer.date) as date;
store customers2 into '/hdfs/output' using PigStorage(',');
This launches a map-reduce job of ~60000 mappers, and 999 reducers.
After the map-reduce job has finished it's work ( I know becuase the output has been written, and the job manager says the job has succeeded ), There is a long pause and I get the following error in the pig output :
2015-11-24 11:45:29,394 [main] INFO org.apache.hadoop.yarn.client.RMProxy - Connecting to ResourceManager at *********
2015-11-24 11:45:29,403 [main] INFO org.apache.hadoop.mapred.ClientServiceDelegate - Application state is completed. FinalApplicationStatus=SUCCEEDED. Redirecting to job history server
2015-11-24 11:46:03,533 [Service Thread] INFO org.apache.pig.impl.util.SpillableMemoryManager - first memory handler call- Usage threshold init = 698875904(682496K) used = 520031456(507843K) committed = 698875904(682496K) max = 698875904(682496K)
2015-11-24 11:46:04,473 [Service Thread] INFO org.apache.pig.impl.util.SpillableMemoryManager - first memory handler call - Collection threshold init = 698875904(682496K) used = 575405920(561919K) committed = 698875904(682496K) max = 698875904(682496K)
2015-11-24 11:47:36,255 [main] ERROR org.apache.pig.tools.grunt.Grunt - ERROR 2998: Unhandled internal error. GC overhead limit exceeded
The stack trace looks something like (each time the exception in is another function ):
Pig Stack Trace
---------------
ERROR 2998: Unhandled internal error. Java heap space
java.lang.OutOfMemoryError: Java heap space
at org.apache.hadoop.mapreduce.v2.api.records.impl.pb.CounterGroupPBImpl.initCounters(CounterGroupPBImpl.java:136)
at org.apache.hadoop.mapreduce.v2.api.records.impl.pb.CounterGroupPBImpl.getAllCounters(CounterGroupPBImpl.java:121)
at org.apache.hadoop.mapreduce.TypeConverter.fromYarn(TypeConverter.java:240)
at org.apache.hadoop.mapreduce.TypeConverter.fromYarn(TypeConverter.java:367)
at org.apache.hadoop.mapreduce.TypeConverter.fromYarn(TypeConverter.java:388)
at org.apache.hadoop.mapred.ClientServiceDelegate.getTaskReports(ClientServiceDelegate.java:448)
at org.apache.hadoop.mapred.YARNRunner.getTaskReports(YARNRunner.java:551)
at org.apache.hadoop.mapreduce.Job$3.run(Job.java:533)
at org.apache.hadoop.mapreduce.Job$3.run(Job.java:531)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1594)
at org.apache.hadoop.mapreduce.Job.getTaskReports(Job.java:531)
at org.apache.pig.backend.hadoop.executionengine.shims.HadoopShims.getTaskReports(HadoopShims.java:235)
at org.apache.pig.tools.pigstats.mapreduce.MRJobStats.addMapReduceStatistics(MRJobStats.java:352)
at org.apache.pig.tools.pigstats.mapreduce.MRPigStatsUtil.addSuccessJobStats(MRPigStatsUtil.java:233)
at org.apache.pig.tools.pigstats.mapreduce.MRPigStatsUtil.accumulateStats(MRPigStatsUtil.java:165)
at org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MapReduceLauncher.launchPig(MapReduceLauncher.java:360)
at org.apache.pig.backend.hadoop.executionengine.HExecutionEngine.launchPig(HExecutionEngine.java:280)
at org.apache.pig.PigServer.launchPlan(PigServer.java:1390)
...
My set of SET statements in the pig script :
SET mapreduce.map.java.opts '-server -Xmx6144m -Djava.net.preferIPv4Stack=true -Duser.timezone=UTC'
SET mapreduce.reduce.java.opts '-server -Xmx6144m -Djava.net.preferIPv4Stack=true -Duser.timezone=UTC'
SET mapreduce.map.memory.mb '8192'
SET mapreduce.reduce.memory.mb '8192'
SET mapreduce.map.speculative 'true'
SET mapreduce.reduce.speculative 'true'
SET mapreduce.jobtracker.maxtasks.perjob '100000'
SET mapreduce.job.split.metainfo.maxsize '-1'
Why is this happening, and how can I fix that ?
Thanks in advance for any help.
Looks like this is caused in your application manager, since you mention that the error is being returned after the execution of all mappers/reducers. Try increasing the memory of application-manager.
In a YARN cluster, you can use the following two properties to control the amount of memory available to your ApplicationMaster:
yarn.app.mapreduce.am.command-opts
yarn.app.mapreduce.am.resource.mb
Again, you could set -Xmx (in the former) to 75% of the resource.mb value.
Details regarding the parameters can be found here.
I am running Hadoop 0.21.0 in a single node cluster to process a single big > 200 GB file. For decreasing the execution time, I have tried different HDFS block sizes ( 128, 256, 512 MB, 1, 1.5, 1.75 GB ) respectively. However, I have got the following exception when using block size >= 2 GB.
Note: I am using java-8-oracle.
2015-08-05 12:02:12,524 WARN org.apache.hadoop.mapred.Child: Exception running child : java.lang.IndexOutOfBoundsException
at org.apache.hadoop.fs.FSInputChecker.read(FSInputChecker.java:186)
at org.apache.hadoop.hdfs.BlockReader.read(BlockReader.java:113)
at org.apache.hadoop.hdfs.DFSInputStream.readBuffer(DFSInputStream.java:466)
at org.apache.hadoop.hdfs.DFSInputStream.read(DFSInputStream.java:517)
at java.io.DataInputStream.readFully(DataInputStream.java:195)
at java.io.DataInputStream.readFully(DataInputStream.java:169)
at org.apache.hadoop.io.SequenceFile$Reader.init(SequenceFile.java:1518)
at org.apache.hadoop.io.SequenceFile$Reader.<init>(SequenceFile.java:1483)
at org.apache.hadoop.io.SequenceFile$Reader.<init>(SequenceFile.java:1451)
at org.apache.hadoop.io.SequenceFile$Reader.<init>(SequenceFile.java:1432)
at org.apache.hadoop.mapreduce.lib.input.SequenceFileRecordReader.initialize(SequenceFileRecordReader.java:60)
at org.apache.hadoop.mapred.MapTask$NewTrackingRecordReader.initialize(MapTask.java:460)
at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:651)
at org.apache.hadoop.mapred.MapTask.run(MapTask.java:328)
at org.apache.hadoop.mapred.Child$4.run(Child.java:217)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:742)
at org.apache.hadoop.mapred.Child.main(Child.java:211)
For the Hadoop version you are using(0.21.0) seems so.
The issue you have was fixed for the next version, see more here: https://issues.apache.org/jira/browse/HDFS-96