metron-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Nick Allen <>
Subject [DISCUSS] Batch Profiler Feature Branch
Date Wed, 19 Sep 2018 15:14:46 GMT
I would like to open a discussion to get the Batch Profiler feature branch
merged into master as part of METRON-1699 [1] Create Batch Profiler.  All
of the work that I had in mind for our first draft of the Batch Profiler
has been completed.  Please take a look through what I have and let me know
if there are other features that you think are required *before* we merge.

Previous list discussions on this topic include [2] and [3].

(Q) What can I do with the feature branch?

  * With the Batch Profiler, you can backfill/seed profiles using archived
telemetry.  This enables the following types of use cases.

      1. As a Security Data Scientist, I want to understand the historical
behaviors and trends of a profile that I have created so that I can
determine if I have created a feature set that has predictive value for
model building.

      2. As a Security Data Scientist, I want to understand the historical
behaviors and trends of a profile that I have created so that I can
determine if I have defined the profile correctly and created a feature set
that matches reality.

      3. As a Security Platform Engineer, I want to generate a profile
using archived telemetry when I deploy a new model to production so that
models depending on that profile can function on day 1.

  * METRON-1699 [1] includes a more detailed description of the feature.

(Q) What work was completed?

  * The Batch Profiler runs on Spark and was implemented in Java to remain
consistent with our current Java-heavy code base.

  * The Batch Profiler is executed from the command-line. It can be
launched using a script or by calling `spark-submit`, which may be useful
for advanced users.

  * Input telemetry can be consumed from multiple sources; for example HDFS
or the local file system.

  * Input telemetry can be consumed in multiple formats; for example JSON
or ORC.

  * The 'output' profile measurements are persisted in HBase and is
consistent with the Storm Profiler.

  * It can be run on any underlying engine supported by Spark. I have
tested it both in 'local' mode and on a YARN cluster.

  * It is installed automatically by the Metron MPack.

  * A README was added that documents usage instructions.

  * The existing Profiler code was refactored so that as much code as
possible is shared between the 3 Profiler ports; Storm, the Stellar REPL,
and Spark.  For example, the logic which determines the timestamp of a
message was refactored so that it could be reused by all ports.

      * metron-profiler-common: The common Profiler code shared amongst
each port.
      * metron-profiler-storm: Profiler on Storm
      * metron-profiler-spark: Profiler on Spark
      * metron-profiler-repl: Profiler on the Stellar REPL
      * metron-profiler-client: The client code for retrieving profile
data; for example PROFILE_GET.

  * There are 3 separate RPM and DEB packages now created for the Profiler.

      * metron-profiler-storm-*.rpm
      * metron-profiler-spark-*.rpm
      * metron-profiler-repl-*.rpm

  * The Profiler integration tests were enhanced to leverage the Profiler
Client logic to validate the results.

  * Review METRON-1699 [1] for a complete break-down of the tasks that have
been completed on the feature branch.

(Q) What limitations exist?

  * You must manually install Spark to use the Batch Profiler.  The Metron
MPack does not treat Spark as a Metron dependency and so does not install
it automatically.

  * You do not configure the Batch Profiler in Ambari.  It is configured
and executed completely from the command-line.

  * To run the Batch Profiler in 'Full Dev', you have to take the following
manual steps. Some of these are arguably limitations with how Ambari
installs Spark 2 in the version of HDP that we run.

      1. Install Spark 2 using Ambari.

      2. Tell Spark how to talk with HBase.

        cp /usr/hdp/current/hbase-client/conf/hbase-site.xml

      3. Create the Spark History directory in HDFS.

        export HADOOP_USER_NAME=hdfs
        hdfs dfs -mkdir /spark2-history

      4. Change the default input path to `hdfs://localhost:8020/...` to
match the port defined by HDP, instead of port 9000.


  • Unnamed multipart/alternative (inline, None, 0 bytes)
View raw message