The indexing topology is extremely simple. Data is ingested into kafka and sent to
/apps/metron/enrichment/indexed
By default, errors during indexing are sent back into the indexing
kafka queue so that they can be indexed and archived.
The sensor specific configuration is intended to configure the indexing used for a given sensor type (e.g. snort
).
Just like the global config, the format is a JSON stored in zookeeper and on disk at $METRON_HOME/config/zookeeper/indexing
. Within the sensor-specific configuration, you can configure the individual writers. The writers currently supported are:
elasticsearch
hdfs
solr
Depending on how you start the indexing topology, it will have either elasticsearch or solr and hdfs writers running.
The configuration for an individual writer-specific configuration is a JSON map with the following fields:
index
: The name of the index to write to (defaulted to the name of the sensor).batchSize
: The size of the batch that is written to the indices at once (defaulted to 1
).enabled
: Whether the writer is enabled (default true
).For a given sensor, the following scenarios would be indicated by the following cases:
{ }
or no file at all.
If a writer config is unspecified, then a warning is indicated in the Storm console. e.g.: WARNING: Default and (likely) unoptimized writer config used for hdfs writer and sensor squid
{ "elasticsearch": { "index": "foo", "batchSize" : 100, "enabled" : true }, "hdfs": { "index": "foo", "batchSize": 1, "enabled" : true } }
{ "elasticsearch": { "index": "foo", "enabled" : true }, "hdfs": { "index": "foo", "batchSize": 100, "enabled" : false } }
Default installed Metron is untuned for production deployment. By far and wide, the most likely piece to require TLC from a performance perspective is the indexing layer. An index that does not keep up will back up and you will see errors in the kafka bolt. There are a few knobs to tune to get the most out of your system.
The indexing
kafka queue is a collection point from the enrichment topology. As such, make sure that the number of partitions in the kafka topic is sufficient to handle the throughput that you expect.
The indexing
topology as started by the $METRON_HOME/bin/start_elasticsearch_topology.sh
or $METRON_HOME/bin/start_solr_topology.sh
script uses a default of one executor per bolt. In a real production system, this should be customized by modifying the flux file in $METRON_HOME/flux/indexing/remote.yaml
.
parallelism
field to the bolts to give Storm a parallelism hint for the various components. Give bolts which appear to be bottlenecks (e.g. the indexing bolt) a larger hint.parallelism
field to the kafka spout which matches the number of partitions for the enrichment kafka queue.topology.workers
field for the topology.Finally, if workers and executors are new to you or you don't know where to modify the flux file, the following might be of use to you:
Zeppelin notebooks can be added to /src/main/config/zeppelin/
(and subdirectories can be created for organization). The placed files must be .json files and be named appropriately. These files must be added to the metron.spec file and the RPMs rebuilt to be available to be loaded into Ambari.
The notebook files will be found on the server in $METRON_HOME/config/zeppelin
The Ambari Management Pack has a custom action to load these templates, ZEPPELIN_DASHBOARD_INSTALL, that will import them into Zeppelin.