This modules holds shared code for indexing activities.
Indices are written in batch and the batch size and batch timeout are specified in the Sensor Indexing Configuration via the batchSize and batchTimeout parameters. These configs are variable by sensor type.
At minimum, a message should have a source.type field. This field is used to lookup the Sensor Indexing Configuration for that message.
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:
elasticsearchhdfssolrDepending on how you start the indexing topology, it will have either Elasticsearch or Solr and HDFS writers running.
| Property | Description | Default Value |
|---|---|---|
index | The name of the index to write to. | Defaults to the name of the sensor. |
batchSize | The size of the batch that is written to the indices at once. | Defaults to 1; no batching. |
batchTimeout | The timeout after which a batch will be flushed even if batchSize has not been met. | Defaults to a duration which is a fraction of the Storm parameter topology.message.timeout.secs, if left undefined or set to 0. Ignored if batchSize is 1, since this disables batching. |
enabled | A boolean indicating whether the writer is enabled. | Defaults to true |
fieldNameConverter | Defines how field names are transformed before being written to the index. Only applicable to elasticsearch. | Defaults to DEDOT. Acceptable values are DEDOT that replaces all ‘.’ with ‘:’ or NOOP that does not change the field names . |
Alerts can be grouped, after appropriate searching, into a set of alerts called a meta alert. A meta alert is useful for maintaining the context of searching and grouping during further investigations. Standard searches can return meta alerts, but grouping and other aggregation or sorting requests will not, because there‘s not a clear way to aggregate in many cases if there are multiple alerts contained in the meta alert. All meta alerts will have the source type of metaalert, regardless of the contained alert’s origins.
Metron comes with built-in templates for the default sensors for Elasticsearch. When adding a new sensor, it will be necessary to add a new template defining the output fields appropriately. In addition, there is a requirement for a field alert of type nested for Elasticsearch 2.x installs. This is detailed at Using Metron with Elasticsearch 2.x.
Metron is configured by default to let Elasticsearch use auto-generated ids for performance reasons. However, due to Storm's at least once processing guarantee, it is possible for duplicate messages to be indexed when messages are replayed for whatever reason. If this scenario is less desirable, the Metron generated id stored in the guid field of the message can be used instead. This can be configured for individual sensors by setting the setDocumentId setting to true in the Sensor Indexing Configuration:
{
"elasticsearch": {
"enabled": true,
"index": "bro",
"setDocumentId": true
}
}
This can also be set for all sensors in the Global Configuration by setting the indexing.writer.elasticsearch.setDocumentId setting to true. These settings are applied in this order of precedence (highest to lowest):
Metron comes with built-in schemas for the default sensors for Solr. When adding a new sensor, it will be necessary to add a new schema defining the output fields appropriately. In addition, these fields are used internally by Metron and also required:
<field name="guid" type="string" indexed="true" stored="true" required="true" multiValued="false" /><field name="source.type" type="string" indexed="true" stored="true" /><field name="timestamp" type="timestamp" indexed="true" stored="true" /><field name="comments" type="string" indexed="true" stored="true" multiValued="true"/><field name="metaalerts" type="string" multiValued="true" indexed="true" stored="true"/>The unique key should be set to guid by including <uniqueKey>guid</uniqueKey> in the schema.
It is strongly suggested the fieldTypes match those in the built-in schemas.
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,
"batchTimeout" : 0,
"enabled" : true
},
"hdfs": {
"index": "foo",
"batchSize": 1,
"batchTimeout" : 0,
"enabled" : true
}
}
{
"elasticsearch": {
"index": "foo",
"enabled" : true
},
"hdfs": {
"index": "foo",
"batchSize": 100,
"batchTimeout" : 0,
"enabled" : false
}
}
There are clear usecases where we would want to incorporate the capability to update indexed data. Thus far, we have limited capabilities provided to support this use-case:
Put simply, the random access index will be always up-to-date, but the HDFS index will need to be joined to the NoSQL write-ahead log to get current updates.
IndexDao AbstractionThe indices mentioned above as part of Update should be pluggable by the developer so that new write-ahead logs or real-time indices can be supported by providing an implementation supporting the data access patterns.
To support a new index, one would need to implement the org.apache.metron.indexing.dao.IndexDao abstraction and provide update and search capabilities. IndexDaos may be composed and updates will be performed in parallel. This enables a flexible strategy for specifying your backing store for updates at runtime. For instance, currently the REST API supports the update functionality and may be configured with a list of IndexDao implementations to use to support the updates.
Updates with the IndexDao.update method replace the current object with the new object. For partial updates, use IndexDao.patch instead.
HBaseDaoUpdates will be written to HBase. The key structure includes the GUID and sensor type and for each new version, a new column is created with value as the message.
The HBase table and column family are configured via fields in the global configuration.
update.hbase.tableThe HBase table to use for message updates.
update.hbase.cfThe HBase column family to use for message updates.
MetaAlertDaoThe goal of meta alerts is to be able to group together a set of alerts while being able to transparently perform actions like searches, as if meta alerts were normal alerts. org.apache.metron.indexing.dao.metaalert.MetaAlertDao extends IndexDao and enables several features:
The implementation of this is to denormalize the relationship between alerts and meta alerts, and store alerts as a nested field within a meta alert. The use of nested fields is to avoid the limitations of parent-child relationships (one-to-many) and merely linking by IDs (which causes issues with pagination as a result of being unable to join indices). A list of containing meta alerts is stored on an alert for the purpose of keeping source alerts and alerts contained in meta alerts in sync.
The search functionality of IndexDao is wrapped by the MetaAlertDao in order to provide both regular and meta alerts side-by-side with sorting. The updating capabilities are similarly wrapped, in order to ensure updates are carried through both the alerts and associated meta alerts. Both of these functions are handled under the hood.
In addition, API endpoints have been added to expose the features listed above. The denormalization handles the case of going from meta alert to alert automatically.
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 streaming platform (Storm logs for example).
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.
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.