From https://www.elastic.co/products/elasticsearch:

Elasticsearch is a distributed, RESTful search and analytics engine capable of addressing a growing number of use cases. As the heart of the Elastic Stack, it centrally stores your data for lightning fast search, fine‑tuned relevancy, and powerful analytics that scale with ease.



Starting in Security Onion 2.4, most data is associated with a data stream, which is an abstraction from traditional indices that leverages one or more backing indices to manage and represent the data within the data stream. The usage of data streams allows for greater flexibility in data management.

Data streams can be targeting during search or other operations directly, similar to how indices are targeted.

For example, a CLI-based query against Zeek connection records would look like the following:

so-elasticsearch-query logs-zeek-so/_search?q=event.dataset:conn

When this query is run against the backend data, it is actually targeting one or more backing indices, such as:


Similarly, you can target a single backing index with the following query:

so-elasticsearch-query .ds-logs-zeek-so-2022-03-08.001/_search?q=event.dataset:conn

You can learn more about data streams at https://www.elastic.co/guide/en/elasticsearch/reference/current/data-streams.html.


Security Onion tries to adhere to the Elastic Common Schema wherever possible. Otherwise, additional fields or slight modifications to native Elastic field mappings may be found within the data.


In Security Onion 2.4, Elasticsearch data is handled partially by both Curator and ILM (https://www.elastic.co/guide/en/elasticsearch/reference/current/index-lifecycle-management.html).

Only Curator performs the following actions:

  • closing of open indices
  • size-based index deletion
  • size-based closed index deletion

Only ILM performs the following actions:

  • size-based index rollover
  • time-based index rollover
  • time-based content tiers

Both Curator and ILM perform the following actions:

  • time-based open index deletion
  • time-based closed index deletion

Default ILM policies are preconfigured and associated with various data streams and index templates in /opt/so/saltstack/default/salt/elasticsearch/defaults.yaml.


You can query Elasticsearch using web interfaces like Alerts, Dashboards, Hunt, and Kibana. You can also query Elasticsearch from the command line using a tool like curl. You can also use so-elasticsearch-query.


You can authenticate to Elasticsearch using the same username and password that you use for Security Onion Console (SOC).

You can add new user accounts to both Elasticsearch and Security Onion Console (SOC) at the same time as shown in the Adding Accounts section. Please note that if you instead create accounts directly in Elastic, then those accounts will only have access to Elastic and not Security Onion Console (SOC).

Diagnostic Logging

  • Elasticsearch logs can be found in /opt/so/log/elasticsearch/.
  • Logging configuration can be found in /opt/so/conf/elasticsearch/log4j2.properties.

Depending on what you’re looking for, you may also need to look at the Docker logs for the container:

sudo docker logs so-elasticsearch


All of the data Elasticsearch collects is stored under /nsm/elasticsearch/.


Elasticsearch receives unparsed logs from Logstash or Elastic Agent. Elasticsearch then parses and stores those logs. Parsers are stored in /opt/so/conf/elasticsearch/ingest/. Custom ingest parsers can be placed in /opt/so/saltstack/local/salt/elasticsearch/files/ingest/. To make these changes take effect, restart Elasticsearch using so-elasticsearch-restart.

Elastic Agent may pre-parse or act on data before the data reaches Elasticsearch, altering the data stream or index to which it is written, or other characteristics such as the event dataset or other pertinent information. This configuration is maintained in the agent policy or integration configuration in Elastic Fleet.


For more about Elasticsearch ingest parsing, please see:


Fields are mapped to their appropriate data type using templates. When making changes for parsing, it is necessary to ensure fields are mapped to a data type to allow for indexing, which in turn allows for effective aggregation and searching in Dashboards, Hunt, and Kibana. Elasticsearch leverages both component and index templates.

Component Templates

From https://www.elastic.co/guide/en/elasticsearch/reference/current/index-templates.html:

Component templates are reusable building blocks that configure mappings, settings, and aliases. While you can use component templates to construct index templates, they aren’t directly applied to a set of indices.

Also see https://www.elastic.co/guide/en/elasticsearch/reference/current/indices-component-template.html.

Index Templates

From https://www.elastic.co/guide/en/elasticsearch/reference/current/index-templates.html:

An index template is a way to tell Elasticsearch how to configure an index when it is created. Templates are configured prior to index creation. When an index is created - either manually or through indexing a document - the template settings are used as a basis for creating the index. Index templates can contain a collection of component templates, as well as directly specify settings, mappings, and aliases.

In Security Onion, component templates are stored in /opt/so/saltstack/default/salt/elasticsearch/templates/component/.

These templates are specified to be used in the index template definitions in /opt/so/saltstack/default/salt/elasticsearch/defaults.yml.

Community ID

For logs that don’t naturally include Community ID, we use the Elasticsearch Community ID processor:


You can configure Elasticsearch by going to Administration –> Configuration –> elasticsearch.


field expansion matches too many fields

If you get errors like failed to create query: field expansion for [*] matches too many fields, limit: 3500, got: XXXX, then this usually means that you’re sending in additional logs and so you have more fields than our default max_clause_count value. To resolve this, you can go to Administration –> Configuration –> elasticsearch –> config –> indices –> query –> bool –> max_clause_count and adjust the value for any boxes running Elasticsearch in your deployment.


Here are a few tips from https://www.elastic.co/blog/how-many-shards-should-i-have-in-my-elasticsearch-cluster:

TIP: Avoid having very large shards as this can negatively affect the cluster’s ability to recover from failure. There is no fixed limit on how large shards can be, but a shard size of 50GB is often quoted as a limit that has been seen to work for a variety of use-cases.

TIP: Small shards result in small segments, which increases overhead. Aim to keep the average shard size between a few GB and a few tens of GB. For use-cases with time-based data, it is common to see shards between 20GB and 40GB in size.

TIP: The number of shards you can hold on a node will be proportional to the amount of heap you have available, but there is no fixed limit enforced by Elasticsearch. A good rule-of-thumb is to ensure you keep the number of shards per node below 20 to 25 per GB heap it has configured. A node with a 30GB heap should therefore have a maximum of 600-750 shards, but the further below this limit you can keep it the better. This will generally help the cluster stay in good health.

To see your existing shards, run the following command and the number of shards will be shown in the fifth column:

sudo so-elasticsearch-query _cat/indices

If you want to view the detail for each of those shards:

sudo so-elasticsearch-query _cat/shards

Given the sizing tips above, if any of your indices are averaging more than 50GB per shard, then you should probably increase the shard count until you get below that recommended maximum of 50GB per shard.

The number of shards for an index can be adjusted by going to Administration –> Configuration –> elasticsearch –> index_settings –> so-INDEX-NAME –> index_template –> template –> settings –> index –> number_of_shards.

Please keep in mind that old indices will retain previous shard settings and the above settings will only be applied to newly created indices.

Heap Size

If total available memory is 8GB or greater, Setup configures the heap size to be 33% of available memory, but no greater than 25GB. You may need to adjust the value for heap size depending on your system’s performance. You can modify this by going to Administration –> Configuration –> elasticsearch –> esheap.

Field limit

Security Onion currently defaults to a field limit of 5000. If you receive error messages from Logstash, or you would simply like to increase this, you can do so by going to Administration –> Configuration –> elasticsearch –> index_settings –> so-INDEX-NAME –> index_template –> template –> settings –> index –> mapping –> total_fields –> limit.

Please note that the change to the field limit will not occur immediately, only on index creation.

Closing Indices

Elasticsearch indices are closed based on the close setting shown at Administration –> Configuration –> elasticsearch –> index_settings –> so-INDEX-NAME –> close. This setting configures Curator to close any index older than the value given. The more indices are open, the more heap is required. Having too many open indices can lead to performance issues. There are many factors that determine the number of days you can have in an open state, so this is a good setting to adjust specific to your environment.

Deleting Indices

Size-based Index Deletion

Size-based deletion of Elasticsearch indices occurs based on the value of cluster-wide elasticsearch.retention.retention_pct, which is derived from the total disk space available for /nsm/elasticsearch across all nodes in the Elasticsearch cluster. The default value for this setting is 50 percent.

To modify this value, first navigate to Administration -> Configuration. At the top of the page, click the Options menu and then enable the Show all configurable settings, including advanced settings. option. Then navigate to elasticsearch -> retention -> retention_pct. The change will take effect at the next 15 minute interval. If you would like to make the change immediately, you can click the SYNCHRONIZE GRID button under the Options menu at the top of the page.

If your open indices are using more than retention_pct, then Curator will delete old open indices until disk space is back under retention_pct. If your total Elastic disk usage (both open and closed indices) is above retention_pct, then so-curator-closed-delete will delete old closed indices until disk space is back under retention_pct. so-curator-closed-delete does not use Curator because Curator cannot calculate disk space used by closed indices. For more information, see https://www.elastic.co/guide/en/elasticsearch/client/curator/current/filtertype_space.html.

Curator and so-curator-closed-delete run on the same schedule. This might seem like there is a potential to delete open indices before deleting closed indices. However, keep in mind that Curator’s delete.yml is only going to see disk space used by open indices and not closed indices. So if we have both open and closed indices, we may be at retention_pct but Curator’s delete.yml is going to see disk space at a value lower than retention_pct and so it shouldn’t delete any open indices.

For example, suppose our retention_pct is 50%, total disk space is 1TB, and we have 30 days of open indices and 300 days of closed indices. We reach retention_pct and both Curator and so-curator-closed-delete execute at the same time. Curator’s delete.yml will check disk space used but it will see that disk space is at maybe 500GB so it thinks we haven’t reached retention_pct and does not delete anything. so-curator-closed-delete gets a more accurate view of disk space used, sees that we have indeed reached retention_pct, and so it deletes closed indices until we get lower than retention_pct. In most cases, Curator deletion should really only happen if we have open indices without any closed indices.

Time-based Index Deletion

Time-based deletion occurs through the use of the $data_stream.policy.phases.delete.min_age setting within the lifecycle policy tied to each index and is controlled by ILM. It is important to note that size-based deletion takes priority over time-based deletion, as disk may reach retention_pct and indices will be deleted before the min_age value is reached.

Policies can be edited within the SOC administration interface by navigating to Administration -> Configuration -> elasticsearch -> $index -> policy -> phases -> delete -> min_age. Changes will take effect when a new index is created.

Distributed Deployments

Security Onion supports Elastic clustering. In this configuration, Elasticsearch instances join together to create a single cluster. When using Elastic clustering, index deletion is based on the delete settings shown in the global pillar above. The delete settings in the global pillar configure Curator to delete indices older than the value given. For each index, please ensure that the close setting is set to a smaller value than the delete setting.

Let’s discuss the process for determining appropriate delete settings. First, check your indices using so-elasticsearch-query to query _cat/indices. For example:

sudo so-elasticsearch-query _cat/indices | grep 2021.08.26

green open  so-zeek-2021.08.26              rEtb1ERqQcyr7bfbnR95zQ 5 0  2514236      0    2.4gb    2.4gb
green open  so-ids-2021.08.26               d3ySLbRHSJGRQ2oiS4pmMg 1 0     1385    147    3.3mb    3.3mb
green open  so-ossec-2021.08.26             qYf1HWGUSn6fIOlOgFgJOQ 1 0   125333     61  267.1mb  267.1mb
green open  so-elasticsearch-2021.08.26     JH8tOgr3QjaQ-EX08OGEXw 1 0    61170      0   32.7mb   32.7mb
green open  so-firewall-2021.08.26          Qx6_ZQS3QL6VGwIXIQ8mfQ 1 0   508799      0  297.4mb  297.4mb
green open  so-syslog-2021.08.26            3HiYP3fgSPmoV-Nbs3dlDw 1 0   181207      0     27mb     27mb
green open  so-kibana-2021.08.26            C6v6sazHSYiwqq5HxfokQg 1 0      745      0  809.5kb  809.5kb

Adding all the index sizes together plus a little padding results in 3.5GB per day. We will use this as our baseline.

If we look at our total /nsm size for our search nodes (data nodes in Elastic nomenclature), we can calculate how many days open or closed that we can store. The equation shown below determines the proper delete timeframe. Note that total usable space depends on replica counts. In the example below we have 2 search nodes with 140GB for 280GB total of /nsm storage. Since we have a single replica we need to take that into account. The formula for that is:

1 replica = 2 x Daily Index Size 2 replicas = 3 x Daily Index Size 3 replicas = 4 x Daily Index Size

Let’s use 1 replica:

Total Space / copies of data = Usable Space

280 / 2 = 140

Suppose we want a little cushion so let’s make Usable Space = 130

Usable NSM space / Daily Index Size = Days

For our example above lets fill in the proper values:

130GB / 3.5GB = 37.1428571 days rounded down to 37 days

Therefore, we can set all of our delete values to 37.


Re-indexing may need to occur if field data types have changed and conflicts arise. This process can be VERY time-consuming, and we only recommend this if keeping data is absolutely critical.

For more information about re-indexing, please see:


If you want to clear all Elasticsearch data including documents and indices, you can run the so-elastic-clear command.


Elasticsearch 8 no longer includes GeoIP databases by default. We include GeoIP databases for Elasticsearch so that all users will have GeoIP functionality. If your search nodes have Internet access and can reach geoip.elastic.co and storage.googleapis.com, then you can opt-in to database updates if you want more recent information. To do this, add the following to your Elasticsearch Salt config:

        enabled: true

More Information


For more information about Elasticsearch, please see: