GitHub user nickwallen reopened a pull request:
https://github.com/apache/metron/pull/1191
METRON-1772 Support alternative input formats in the Batch Profiler
[Feature Branch]
By default, the Batch Profiler supports the text/json that Metron lands in
HDFS as the source of the archived telemetry. Of course, this is not the best
option for archiving telemetry in many cases and users may choose to store it
in alternative formats.
Alternatives like ORC should be supported when reading the input telemetry
in the Batch Profiler. The user should be able to customize the profiler based
on how they have chosen to archive their telemetry.
- Updated README to describe how to read alternative input formats.
- Added an additional command line option that allows the user to pass
custom options to the `DataFrameReader`. This may be needed by a user
depending on how the telemetry is archived.
- For example, this allows the user to pass reader options like
`quote`, `nullValue`, etc needed by
[csv](https://spark.apache.org/docs/2.3.1/api/java/org/apache/spark/sql/DataFrameReader.html#csv-java.lang.String...-)
or `allowSingleQuote`, `allowComments` needed by
[json](https://spark.apache.org/docs/2.3.1/api/java/org/apache/spark/sql/DataFrameReader.html#json-scala.collection.Seq-)
- Added an integration test that validates that the Batch Profiler can read
ORC data.
- Added an integration test that validates that the Batch Profiler can read
CSV data. I added CSV as a test so that I could validate the user providing
custom options to the `DataFrameReader`.
This is a pull request against the `METRON-1699-create-batch-profiler`
feature branch.
This is dependent on the following PRs. By filtering on the last commit,
this PR can be reviewed before the others are reviewed and merged.
- [ ] #1189
## Testing
1. Stand-up a development environment.
```
cd metron-deployment/development/centos6
vagrant up
vagrant ssh
sudo su -
```
1. Validate the environment by ensuring alerts are visible within the
Alerts UI and that the Metron Service Check in Ambari passes.
1. Allow some telemetry to be archived in HDFS.
```
[root@node1 ~]# hdfs dfs -cat /apps/metron/indexing/indexed/*/* | wc -l
6916
```
1. Shutdown Metron topologies, Storm, Elasticsearch, Kibana, MapReduce2 to
free up some resources on the VM.
1. Use Ambari to install Spark (version 2.3+). Actions > Add Service >
Spark2
1. Make sure Spark can talk to HBase.
```
SPARK_HOME=/usr/hdp/current/spark2-client
cp /usr/hdp/current/hbase-client/conf/hbase-site.xml $SPARK_HOME/conf/
```
1. Follow the [Getting
Started](https://github.com/apache/metron/tree/feature/METRON-1699-create-batch-profiler/metron-analytics/metron-profiler-spark#getting-started)
section of the README to seed a basic profile using the text/json telemetry
that is archived in HDFS.
1. Create the Profile.
```
[root@node1 ~]# source /etc/default/metron
[root@node1 ~]# cat $METRON_HOME/config/zookeeper/profiler.json
{
"profiles": [
{
"profile": "hello-world",
"foreach": "'global'",
"init": { "count": "0" },
"update": { "count": "count + 1" },
"result": "count"
}
],
"timestampField": "timestamp"
}
```
1. Edit the Batch Profiler properties. to point it at the correct input
path (changed localhost:9000 to localhost:8020).
```
[root@node1 ~]# cat
/usr/metron/0.5.1/config/batch-profiler.properties
spark.app.name=Batch Profiler
spark.master=local
spark.sql.shuffle.partitions=8
profiler.batch.input.path=hdfs://localhost:8020/apps/metron/indexing/indexed/*/*
profiler.batch.input.format=text
profiler.period.duration=15
profiler.period.duration.units=MINUTES
```
1. Edit logging as you see fit. For example, set Spark logging to WARN
and Profiler logging to DEBUG. This is described in the README.
1. Run the Batch Profiler.
```
$METRON_HOME/bin/start_batch_profiler.sh
```
1. Launch the Stellar REPL and retrieve the profile data. Save this result
as it will be used for validation in subsequent steps.
```
[root@node1 ~]# $METRON_HOME/bin/stellar -z $ZOOKEEPER
...
Stellar, Go!
Functions are loading lazily in the background and will be unavailable
until loaded fully.
...
[Stellar]>>> window := PROFILE_FIXED(2, "HOURS")
[ProfilePeriod{period=1707332, durationMillis=900000},
ProfilePeriod{period=1707333, durationMillis=900000},
ProfilePeriod{period=1707334, durationMillis=900000},
ProfilePeriod{period=1707335, durationMillis=900000},
ProfilePeriod{period=1707336, durationMillis=900000},
ProfilePeriod{period=1707337, durationMillis=900000},
ProfilePeriod{period=1707338, durationMillis=900000},
ProfilePeriod{period=1707339, durationMillis=900000},
ProfilePeriod{period=1707340, durationMillis=900000}]
[Stellar]>>> PROFILE_GET("hello-world","global", window)
[1020, 5066, 830]
```
1. Delete the profiler data.
```
echo "truncate 'profiler'" | hbase shell
```
1. Create a new directory in HDFS for the ORC data that we are about to
generate.
```
export HADOOP_USER_NAME=hdfs
hdfs dfs -mkdir /apps/metron/indexing/orc
hdfs dfs -chown metron:hadoop /apps/metron/indexing/orc
```
1. You may need to also create this directory for Spark.
```
export HADOOP_USER_NAME=hdfs
hdfs dfs -mkdir /spark2-history
```
1. Launch the Spark shell
```
export SPARK_MAJOR_VERSION=2
export HADOOP_USER_NAME=hdfs
spark-shell
```
1. Use the Spark Shell to transform the text/json telemetry to ORC.
```
scala> val jsonPath =
"hdfs://localhost:8020/apps/metron/indexing/indexed/*/*"
jsonPath: String =
hdfs://localhost:8020/apps/metron/indexing/indexed/*/*
scala> val orcPath = "hdfs://localhost:8020/apps/metron/orc/"
orcPath: String = hdfs://localhost:8020/apps/metron/orc/
scala> val msgs = spark.read.format("text").load(jsonPath).as[String]
msgs: org.apache.spark.sql.Dataset[String] = [value: string]
scala> msgs.count
res0: Long = 6916
scala>
msgs.write.mode("overwrite").format("org.apache.spark.sql.execution.datasources.orc").save(orcPath)
scala>
spark.read.format("org.apache.spark.sql.execution.datasources.orc").load(orcPath).as[String].count
res3: Long = 6916
```
1. Edit `$METRON_HOME/config/batch-profiler.properties` so that the Batch
Profiler consumes that telemetry stored as ORC.
```
[root@node1 ~]# cat /usr/metron/0.5.1/config/batch-profiler.properties
spark.app.name=Batch Profiler
spark.master=local
spark.sql.shuffle.partitions=8
profiler.batch.input.path=hdfs://localhost:8020/apps/metron/orc/
profiler.batch.input.format=org.apache.spark.sql.execution.datasources.orc
profiler.period.duration=15
profiler.period.duration.units=MINUTES
```
1. Again, run the Batch Profiler again. It will now consume the ORC data.
```
$METRON_HOME/bin/start_batch_profiler.sh
```
1. Again, launch the Stellar REPL and retrieve the profile data. The data
should match the previous profile data that was generated using the test/json
telemetry.
```
[root@node1 ~]# $METRON_HOME/bin/stellar -z $ZOOKEEPER
...
Stellar, Go!
Functions are loading lazily in the background and will be unavailable
until loaded fully.
...
[Stellar]>>> window := PROFILE_FIXED(2, "HOURS")
[ProfilePeriod{period=1707332, durationMillis=900000},
ProfilePeriod{period=1707333, durationMillis=900000},
ProfilePeriod{period=1707334, durationMillis=900000},
ProfilePeriod{period=1707335, durationMillis=900000},
ProfilePeriod{period=1707336, durationMillis=900000},
ProfilePeriod{period=1707337, durationMillis=900000},
ProfilePeriod{period=1707338, durationMillis=900000},
ProfilePeriod{period=1707339, durationMillis=900000},
ProfilePeriod{period=1707340, durationMillis=900000}]
[Stellar]>>> PROFILE_GET("hello-world","global", window)
[1020, 5066, 830]
```
1. Notice that the output is exactly the same no matter which input format
we have used.
## Pull Request Checklist
- [x] Have you included steps to reproduce the behavior or problem that is
being changed or addressed?
- [x] Have you included steps or a guide to how the change may be verified
and tested manually?
- [x] Have you ensured that the full suite of tests and checks have been
executed in the root metron folder via:
- [x] Have you written or updated unit tests and or integration tests to
verify your changes?
- [x] If adding new dependencies to the code, are these dependencies
licensed in a way that is compatible for inclusion under [ASF
2.0](http://www.apache.org/legal/resolved.html#category-a)?
- [x] Have you verified the basic functionality of the build by building
and running locally with Vagrant full-dev environment or the equivalent?
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/nickwallen/metron METRON-1772
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/metron/pull/1191.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #1191
----
commit eb419d8bdc6e60900bb08142d4d2bb50b2bd8c23
Author: Nick Allen <nick@...>
Date: 2018-09-06T18:47:28Z
METRON-1770 Add Docs for Running the Profiler with Spark on YARN
commit b17e328fed0f3f51f14383ae48cb526b0993d314
Author: Nick Allen <nick@...>
Date: 2018-09-07T21:07:44Z
METRON-1772 Support alternative input formats in the Batch Profiler
commit 0bd24b02b953b65d7c4b36283973f71ce4715f45
Author: Nick Allen <nick@...>
Date: 2018-09-10T16:15:54Z
Fixed dependencies to avoid clash between Antlr versions
commit 185cf6d9930de215c45f7cc228613daee4fc335f
Author: Nick Allen <nick@...>
Date: 2018-09-10T18:04:23Z
Improved README for using alternative input formats
commit 6f165632d10eb9e8b2ad0414d77ec4ec562ead28
Author: Nick Allen <nick@...>
Date: 2018-09-10T21:28:37Z
Trying to fix IT errors with Antlr
commit 31e7b071e911d867806a77357522442f38bc6884
Author: Nick Allen <nick@...>
Date: 2018-09-10T21:34:32Z
Merge remote-tracking branch
'apache/feature/METRON-1699-create-batch-profiler' into METRON-1772
commit 2cbdb84e323ff0f2ece98659f4be94843bf7f2d5
Author: Nick Allen <nick@...>
Date: 2018-09-18T17:37:38Z
Merge branch 'feature/METRON-1699-create-batch-profiler' into METRON-1772
----
---
