I have some fairly simple Hadoop streaming jobs that look like this:
yarn jar /usr/lib/hadoop-mapreduce/hadoop-streaming-2.2.0.2.0.6.0-101.jar \
-files hdfs:///apps/local/count.pl \
-input /foo/data/bz2 \
-output /user/me/myoutput \
-mapper "cut -f4,8 -d," \
-reducer count.pl \
-combiner count.pl
The count.pl script is just a simple script that accumulates counts in a hash and prints them out at the end - the details are probably not relevant but I can post it if necessary.
The input is a directory containing 5 files encoded with bz2 compression, roughly the same size as each other, for a total of about 5GB (compressed).
When I look at the running job, it has 45 mappers, but they're all running on one node. The particular node changes from run to run, but always only one node. Therefore I'm achieving poor data locality as data is transferred over the network to this node, and probably achieving poor CPU usage too.
The entire cluster has 9 nodes, all the same basic configuration. The blocks of the data for all 5 files are spread out among the 9 nodes, as reported by the HDFS Name Node web UI.
I'm happy to share any requested info from my configuration, but this is a corporate cluster and I don't want to upload any full config files.
It looks like this previous thread [ why map task always running on a single node ] is relevant but not conclusive.
EDIT: at #jtravaglini's suggestion I tried the following variation and saw the same problem - all 45 map jobs running on a single node:
yarn jar \
/usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples-2.2.0.2.0.6.0-101.jar \
wordcount /foo/data/bz2 /user/me/myoutput
At the end of the output of that task in my shell, I see:
Launched map tasks=45
Launched reduce tasks=1
Data-local map tasks=18
Rack-local map tasks=27
which is the number of data-local tasks you'd expect to see on one node just by chance alone.
Related
I have a somewhat basic question about Snakemake parallelization when using cluster execution: can jobs from the same rule be parallelized both within a node and across multiple nodes at the same time?
Let's say for example that I have 100 bwa mem jobs and my cluster has nodes with 40 cores each. Could I run 4 bwa mem per node, each using 10 threads, and then have Snakemake submit 25 separate jobs? Essentially, I want to parallelize both within and across nodes for the same rule.
Here is my current snakefile:
SAMPLES, = glob_wildcards("fastqs/{id}.1.fq.gz")
print(SAMPLES)
rule all:
input:
expand("results/{sample}.bam", sample=SAMPLES)
rule bwa:
resources:
time="4:00:00",
partition="short-40core"
input:
ref="/path/to/reference/genome.fa",
fwd="fastqs/{sample}.1.fq.gz",
rev="fastqs/{sample}.2.fq.gz"
output:
bam="results/{sample}.bam"
log:
"results/logs/bwa/{sample}.log"
params:
threads=10
shell:
"bwa mem -t {params.threads} {input.ref} {input.fwd} {input.rev} 2> {log} | samtools view -bS - > {output.bam}"
I've run this with the following command:
snakemake --cluster "sbatch --partition={resources.partition}" -s bwa_slurm_snakefile --jobs 25
With this setup, I get 25 jobs submitted, each to a different node. However, only one bwa mem process (using 10 threads) is run per node.
Is there some straightforward way to modify this so that I could get 4 different bwa mem jobs (each using 10 threads) to run on each node?
Thanks!
Dave
Edit 07/28/22:
In addition to Troy's suggestion below, I found a straightforward way of accomplishing what I was trying to do by simply following the job grouping documentation.
Specifically, I did the following when executing my Snakemake pipeline:
snakemake --cluster "sbatch --partition={resources.partition}" -s bwa_slurm_snakefile --jobs 25 --groups bwa=group0 --group-components group0=4 --rerun-incomplete --cores 40
By specifying a group ("group0") for the bwa rule and setting "--group-components group0=4", I was able to group the jobs such that 4 bwa runs are occurring on each node.
You can try job grouping but note that resources are typically summed together when submitting group jobs like this. Usually that's not what is desired, but in your case it seems to be correct.
Instead you can make a group job with another rule that does the grouping for you in batches of 4.
rule bwa_mem:
group: 'bwa_batch'
output: '{sample}.bam'
...
def bwa_mem_batch(wildcards):
# for wildcard.i, pick 4 bwa_mem outputs to put in this group
return expand('{sample}.bam', sample=SAMPLES[i*4:i*4+4])
rule bwa_mem_batch:
input: bwa_mem_batch_input
output: touch('flag_{i}') # could be temp too
group 'bwa_batch'
The consuming rule must request flag_{i} for i in {0..len(SAMPLES)//4}. With cluster integration, each slurm job gets 1 bwa_mem_batch job and 4 bwa_mem jobs with resources for a single bwa_mem job. This is useful for batching together multiple jobs to increase the runtime.
As a final point, this may do what you want, but I don't think it will help you get around QOS or other job quotas. You are using the same amount of CPU hours either way. You may be waiting in the queue longer because the scheduler can't find 40 threads to give you at once, where it could have given you a few 10 thread jobs. Instead, consider refining your resource values to get better efficiency, which may get your jobs run earlier.
I need to run a snakemake pipeline on a DRMAA cluster with a total number of >2000 jobs. When some of the jobs have failed, I would like to receive in the end an easy readable summary report, where only the failed jobs are listed instead of the whole job summary as given in the log.
Is there a way to achieve this without parsing the log file by myself?
These are the (incomplete) cluster options:
jobs: 200
latency-wait: 5
keep-going: True
rerun-incomplete: True
restart-times: 2
I am not sure if there is another way than parsing the log file yourself, but I've done it several times with grep and I am happy with the results:
cat .snakemake/log/[TIME].snakemake.log | grep -B 3 -A 3 error
Of course you should change the TIME placeholder for whichever run you want to check.
Are there any specific problems with running Microsoft's BCP utility (on CentOS 7, https://learn.microsoft.com/en-us/sql/linux/sql-server-linux-migrate-bcp?view=sql-server-2017) on multiple threads? Googling could not find much, but am looking at a problem that seems to be related to just that.
Copying a set of large TSV files from HDFS to a remote MSSQL Server with some code of the form
bcpexport() {
filename=$1
TO_SERVER_ODBCDSN=$2
DB=$3
TABLE=$4
USER=$5
PASSWORD=$6
RECOMMEDED_IMPORT_MODE=$7
DELIMITER=$8
echo -e "\nRemoving header from TSV file $filename"
echo -e "Current head:\n"
echo $(head -n 1 $filename)
echo "$(tail -n +2 $filename)" > $filename
echo "First line of file is now..."
echo $(head -n 1 $filename)
# temp. workaround safeguard for NFS latency
#sleep 5 #FIXME: appears to sometimes cause script to hang, workaround implemented below, throws error if timeout reached
timeout 30 sleep 5
echo -e "\nReplacing null literal values with empty chars"
NULL_WITH_TAB="null\t" # WARN: assumes the first field is prime-key so never null
TAB="\t"
sed -i -e "s/$NULL_WITH_TAB/$TAB/g" $filename
echo -e "Lines containing null (expect zero): $(grep -c "\tnull\t" $filename)"
# temp. workaround safeguard for NFS latency
#sleep 5 #FIXME: appears to sometimes cause script to hang, workaround implemented below
timeout 30 sleep 5
/opt/mssql-tools/bin/bcp "$TABLE" in "$filename" \
$TO_SERVER_ODBCDSN \
-U $USER -P $PASSWORD \
-d $DB \
$RECOMMEDED_IMPORT_MODE \
-t "\t" \
-e ${filename}.bcperror.log
}
export -f bcpexport
parallel -q -j 7 bcpexport {} "$TO_SERVER_ODBCDSN" $DB $TABLE $USER $PASSWORD $RECOMMEDED_IMPORT_MODE $DELIMITER \
::: $DATAFILES/$TARGET_GLOB
where $DATAFILES/$TARGET_GLOB constructs a glob that lists a set of files in a directory.
When running this code for a set of TSV files, finding that sometimes some (but not all) of the parallel BCP threads fail, ie. some files successfully copy to MSSQL Server
Starting copy...
5397376 rows copied.
Network packet size (bytes): 4096
Clock Time (ms.) Total : 154902 Average : (34843.8 rows per sec.)
while others output error message
Starting copy...
BCP copy in failed
Usually, see this pattern: a few successful BCP copy-in operations in the first few threads returned, then a bunch of failing threads return their output until run out of files (GNU Parallel returns output only when whole thread done to appear same as if sequential).
Notice in the code there is the -e option to produce an error file for each BCP copy-in operation (see https://learn.microsoft.com/en-us/sql/tools/bcp-utility?view=sql-server-2017#e). When examining the files after observing these failing behaviors, all are blank, no error messages.
Only have seen this with the number of threads >= 10 (and only for certain sets of data (assuming has something to do with total number of files are files sizes, and yet...)), no errors seen so far when using ~7 threads, which further makes me suspect this has something to do with multi-threading.
Monitoring system resources (via free -mh) shows that generally ~13GB or RAM is always available.
May be helpful to note that the data bcp is trying to copy-in may be ~500000-1000000 records long with an upper limit of ~100 columns per record.
Does anyone have any idea what could be going on here? Note, am pretty new to using BCP as well as GNU Parallel and multi-threading.
No, no issues specific to the BCP program being run in multiple threads. You seem to be on the track of what I would say your issue is, system resources. Have you monitored system resources while increasing the number of threads? If anything, there is likely an issue with BCP executing properly when memory/cpu/network resources are low. Regarding the "-e" option, it is meant to output data errors. login errors, bad table names... many other errros are not reported in the file created with the -e option. When you get output using the "-e" option, you'll see info like "value truncated" and such... will give you line numbers and sample data that was at issue.
TLDR: Adding more threads to run concurrently to have bcp copy-in files of data seems to have the affect of overwhelming the endpoint MSSQL Server with write instructions, causing the bcp threads to fail (maybe timeing out?). When the number of threads becomes too many seems to depend on the size of the files getting copy-in'ed by bcp (ie. both the number of records in the file as well as the width of each record (ie. number of columns)).
Long version (more reasons for my theory):
1.
When running a larger number of bcp threads and looking at the processes started on the machine (https://clustershell.readthedocs.io/en/latest/tools/clush.html)
ps -aux | grep bcp
seeing a bunch of sleeping processes (notice the S, see https://askubuntu.com/a/360253/760862) as shown below (added newlines for readability)
me 135296 14.5 0.0 77596 6940 ? S 00:32 0:01
/opt/mssql-tools/bin/bcp TABLENAME in /path/to/tsv/1_16_0.tsv -D -S MyMSSQLServer -U myusername -P -d myDB -c -t \t -e /path/to/logfile
These threads appear to sleep for very long time. Further debugging into why these threads are sleeping suggests that they may in fact be doing their intended job (which would further imply that the problem may be coming from BCP itself (see https://stackoverflow.com/a/52748660/8236733)). From https://unix.stackexchange.com/a/47259/260742 and https://unix.stackexchange.com/a/36200/260742)
A process in S state is usually in a blocking system call, such as reading or writing to a file or the network, or waiting for another called program to finish.
(eg. writing to the MSSQL Server endpoint destination given to bcp in the ODBCDSN)
Your process will be in S state when it is doing reads and possibly writes that are blocking. Can also happen while waiting on semaphores or other synchronization primitives... This is all normal and expected, and not usually a problem... you don't want it to waste CPU while it's waiting for user input.
2. When running different sets of files of varying record-amount-per-file (eg. ranges of 500000 - 1000000 rows/file) and record-width-per-file (~10 - 100 columns/row), found that in cases with either very large data width or amounts, running a fixed set of bcp threads would fail.
Eg. for a set of ~33 TSVs with ~500000 rows each, each row being ~100 columns wide, a set of 30 threads would write the first few OK, but then all the rest would start returning failure messages. Incorporating a bit from #jamie's answer, the fact the the failure messages returned are "BCP copy in failed" errors does not necessarily mean it has do do with the content of the data in question. Having no actual content being written into the -e errorlog files from my process, #jamie's post says this
Regarding the "-e" option, it is meant to output data errors. login errors, bad table names... many other errros are not reported in the file created with the -e option. When you get output using the "-e" option, you'll see info like "value truncated" and such... will give you line numbers and sample data that was at issue.
Meanwhile, a set of ~33 TSVs with ~500000 rows each, each row being ~100 wide, and still using 30 bcp threads would complete quickly and without error (also would be faster when reducing the number of threads or file set). The only difference here being the overall size of the data being bcp copy-in'ed to the MSSQL Server.
All this while
free -mh
still showed that the machine running the threads still had ~15GB of free RAM remaining in each case (which is again why I suspect that the problem has to do with the remote MSSQL Server endpoint rather than with the code or local machine itself).
3. When running some of the tests from (2), found that manually killing the parallel process (via CTL+C) and then trying to remotely truncate the testing table being written to with /opt/mssql-tools/bin/sqlcmd -Q "truncate table mytable" on the local machine would take a very long time (as opposed to manually logging into the MSSQL Server and executing a truncate mytable in the DB). Again this makes me think that this has something to do with the MSSQL Server having too many connections and just being overwhelmed.
** Anyone with any MSSQL Mgmt Studio experience reading this (I have basically none), if you see anything here that makes you think that my theory is incorrect please let me know your thoughts.
I am currently working on making a CICD script to deploy a complex environment into another environment. We have multiple technology involved and I currently want to optimize this script because it's taking too much time to fetch information on each environment.
In the OpenShift 3.6 section, I need to get the last successful deployment for each application for a specific project. I try to find a quick way to do so, but right now I only found this solution :
oc rollout history dc -n <Project_name>
This will give me the following output
deploymentconfigs "<Application_name>"
REVISION STATUS CAUSE
1 Complete config change
2 Complete config change
3 Failed manual change
4 Running config change
deploymentconfigs "<Application_name2>"
REVISION STATUS CAUSE
18 Complete config change
19 Complete config change
20 Complete manual change
21 Failed config change
....
I then take this output and parse each line to know which is the latest revision that have the status "Complete".
In the above example, I would get this list :
<Application_name> : 2
<Application_name2> : 20
Then for each application and each revision I do :
oc rollout history dc/<Application_name> -n <Project_name> --revision=<Latest_Revision>
In the above example the Latest_Revision for Application_name is 2 which is the latest complete revision not building and not failed.
This will give me the output with the information I need which is the version of the ear and the version of the configuration that was used in the creation of the image use for this successful deployment.
But since I have multiple application, this process can take up to 2 minutes per environment.
Would anybody have a better way of fetching the information I required?
Unless I am mistaken, it looks like there are no "one liner" with the possibility to get the information on the currently running and accessible application.
Thanks
Assuming that the currently active deployment is the latest successful one, you may try the following:
oc get dc -a --no-headers | awk '{print "oc rollout history dc "$1" --revision="$2}' | . /dev/stdin
It gets a list of deployments, feeds it to awk to extract the name $1 and revision $2, then compiles your command to extract the details, finally sends it to standard input to execute. It may be frowned upon for not using xargs or the like, but I found it easier for debugging (just drop the last part and see the commands printed out).
UPDATE:
On second thoughts, you might actually like this one better:
oc get dc -a -o jsonpath='{range .items[*]}{.metadata.name}{"\n\t"}{.spec.template.spec.containers[0].env}{"\n\t"}{.spec.template.spec.containers[0].image}{"\n-------\n"}{end}'
The example output:
daily-checks
[map[name:SQL_QUERIES_DIR value:daily-checks/]]
docker-registry.default.svc:5000/ptrk-testing/daily-checks#sha256:b299434622b5f9e9958ae753b7211f1928318e57848e992bbf33a6e9ee0f6d94
-------
jboss-webserver31-tomcat
registry.access.redhat.com/jboss-webserver-3/webserver31-tomcat7-openshift#sha256:b5fac47d43939b82ce1e7ef864a7c2ee79db7920df5764b631f2783c4b73f044
-------
jtask
172.30.31.183:5000/ptrk-testing/app-txeq:build
-------
lifebicycle
docker-registry.default.svc:5000/ptrk-testing/lifebicycle#sha256:a93cfaf9efd9b806b0d4d3f0c087b369a9963ea05404c2c7445cc01f07344a35
You get the idea, with expressions like .spec.template.spec.containers[0].env you can reach for specific variables, labels, etc. Unfortunately the jsonpath output is not available with oc rollout history.
UPDATE 2:
You could also use post-deployment hooks to collect the data, if you can set up a listener for the hooks. Hopefully the information you need is inherited by the PODs. More info here: https://docs.openshift.com/container-platform/3.10/dev_guide/deployments/deployment_strategies.html#lifecycle-hooks
I used to work with a cluster using SLURM scheduler, but now I am more or less forced to switch to a SGE-based cluster, and I'm trying to get a hang of it. The thing I was working on SLURM system involves running an executable using N input files, and set a SLURM configuration file in this fashion,
slurmConf.conf SLURM configuration file
0 /path/to/exec /path/to/input1
1 /path/to/exec /path/to/input2
2 /path/to/exec /path/to/input3
3 /path/to/exec /path/to/input4
4 /path/to/exec /path/to/input5
5 /path/to/exec /path/to/input6
6 /path/to/exec /path/to/input7
7 /path/to/exec /path/to/input8
8 /path/to/exec /path/to/input9
9 /path/to/exec /path/to/input10
And my working submission script in SLURM contains this line;
srun -n $SLURM_NNODES --multi-prog $slconf
$slconf refers to a path to that configuration file
This setup worked as I wanted - to run the executable with 10 different inputs at the same time with 10 nodes. Now that I just transitioned to SGE system, I want to do the same thing but I tried to read the manual and found nothing quite like SLURM. Could you please give me some light on how to achieve the same thing on SGE system?
Thank you very much!
You could use the "job array" feature of the Grid Engine.
Create a shell script sge_job.sh
#!/bin/sh
#
# sge_job.sh -- SGE job description script
#
#$ -t 1-10
/path/to/exec /path/to/input$SGE_TASK_ID
And submit this script to SGE with qsub.
qsub sge_job.sh
Dmitri Chubarov's answer is excellent, and the most robust way to proceed as it places less load on the submit node when submitting many jobs (>1000). Alternatively, you can wrap qsub in a for loop:
for i in {1..10}
do
echo "/path/to/exec /path/to/input${i}" | qsub
done
I sometimes use the above when whatever varies as input is not easily captured as a range of integers.
Example:
for f in `ls /some/path/input*`
do
echo "/path/to/exec ${f}" | qsub
done