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Version: VAST v3.0


VAST has powerful features for transforming data in motion and data at rest. Both aspects rely on pipelines as building block.

Define a pipeline

To reference and use a pipeline as building block, you must add give it a unique name under the key vast.pipelines in the configuration file:

example: |
hash --salt="B3IwnumKPEJDAA4u" src_ip
| summarize

This example pipeline consists of two operators, hash and summarize that execute in sequential order.

Have a look at all available operators to understand what other transformations you can apply.

Modify data in motion

VAST supports import and export pipelines. The former apply to new data ingested into the system, the latter apply to the results of a VAST query. Both import and export pipelines can run in the server or client process. For imports, the client is the source generating the data, for exports the client is the sink receiving the exported data.

The flexible combination of location and type of pipeline type enables multiple use cases:

LocationTypeUse caseExample
ClientImportEnrichmentAdd community ID to flow telemetry
ServerImportComplianceAnonymize PII data
ClientExportPost-processingCompute expensive function (e.g., string entropy)
ServerExportAccess controlRemove sensitive fields

Deploying a pipeline involves involves two separate configuration steps:

  1. Define the pipeline
  2. Trigger the pipeline

The trigger determines when and where a pipeline executes. Here is an example configuration:

example_pipeline: |
| operator2
- pipeline: example_pipeline
location: server
events: [intel.ioc, zeek.conn]

Triggers are defined under configuration key vast.pipeline-triggers. The two subkeys import and export specify the pipeline type the trigger itself is a dictionary with three keys:

  1. pipeline: the name one of a previously defined pipeline
  2. location: either server or client
  3. events: a list of event types for which the pipeline fires

The above example configures example_pipeline to run at on the server side during import for the two events intel.ioc and zeek.conn.

Modify data at rest

Delete old data when reaching storage quota

VAST's disk-monitoring feature enables periodic deletion of events based on utilized disk storage. To limit the disk space used by the VAST database, configure a disk quota:

vast start --disk-quota-high=1TiB

Whenever VAST detects that its database directory has grown to exceed the configured quota, it will erase the oldest data in the database. It is possible to specify an additional --disk-quota-low option to define a corridor for the disk space usage. This can be used to avoid having VAST running permanently at the upper limit and to instad batch the deletion operations together.

The full set of available options looks like this:

# Triggers removal of old data when the DB dir exceeds the disk budget.
disk-budget-high: 0K
# When the DB dir exceeds the budget, VAST erases data until the directory size
# falls below this value.
disk-budget-low: 0K
# Seconds between successive disk space checks.
disk-budget-check-interval: 90

When using this method, we recommend placing the log file outside of the database directory. It counts towards the size calculations, but cannot be automatically deleted during a deletion cycle.

Transform old data when reaching storage quota

Instead of just deleting data periodically, VAST can also trigger spatial compaction when exceeding a given disk budget. A spatial compaction cycle transforms data until disk usage falls below the budget, e.g., by removing columns or rows from certain events, or by deleting them entirely.

When the disk budget exceeds the configured threshold, VAST decides what data to compact. The compaction mode defines how this happens. Currently, there exists only one mode: weighted age.

This compaction mode selects all events according to a weighted age. To compute the weighted age, VAST divides the actual age of an event with the weight assigned to this event type. For example, applying a weight of 100 to an event that is 100 days old would yield a weighted age of 1 day. This causes it to be transformed after events that are 50 days old. Conversely, a weights less than one results in an older weighted age, resulting in earlier consideration in a compaction cycle.

The default weight is 1 for all event types. Here is an example configuration that adjusts the weights:

plugins: [compaction]
mode: weighted-age
interval: 6 hours
disk-budget-high: 10TiB
disk-budget-low: 8TiB
- weight: 0.1
types: [suricata.flow]
#pipeline: fancy_flow_compaction
- weight: 100
types: [suricata.alert]
#pipeline: fancy_alert_compaction

The pipeline key for each type is optional. If present, the corresponding pipeline processes all matching events. If absent, VAST deletes matching events.

Two additional keys are useful to fine-tune the behavior of the compaction plugin:

  1. an absolute path to a binary that should be executed to determine the current disk usage
  2. adjust how many compaction candidates should be processed before re-checking the size of the database directory

Transform data after exceeding a retention span

VAST triggers temporal compaction according to a set of rules that define how to transform events after they reach a specfic age. This declarative specification makes it easy to express fine-grained data retention policies, which is often needed for regulatory requirements and compliance.

For each compaction cycle, VAST processes all rules and identifies what subset of the data has become subject to transformation. To this end, each rule defines a minimum age, i.e., a lower bound that must be exceeded before the corresponding events undergo their configured pipeline.

To configure temporal compaction, provide a list of compaction rules under the key plugins.compaction.time in the VAST configuration. A compaction rule defines the minimum age using key after, the pipeline to apply with the key pipeline, the scope in terms of schema using the key types, and a name to uniquely refer to the rule. Omitting the types key causes temporal compaction rules to be applied to all schemas.

By default, a compaction rule consumes its input, i.e. it erases the original events from the database and replaces them with the transformed events. The preserve-input option can be specified on a temporal compaction rule to override this behavior and to keep the input partitions available.


VAST applies each rule only once per partition and stores the applied rule name within the partition meta data. If you rename a rule in the configuration and reload a new compaction configuration, already compacted partitions will undergo another round of compaction.

The pipelines referenced in the compaction configuration must be defined in the VAST configuration.

plugins: [compaction]
anonymize_urls: |
replace net.url="xxx"
aggregate_flows: |
10 mins
# How often to check the `after` condition below.
interval: 1 day
- after: 2 days
name: uri_scrubbing
pipeline: anonymize_urls
- zeek.http
- suricata.http
- after: 7 days
name: flow_reduction
pipeline: aggregate_flows
keep: true
- suricata.flow

Trigger a compaction manually

You can also interact with the compaction plugin on the command line, through the compaction subcommand. Use the list subcommand to show all configured compaction rules:

vast compaction list

You can then trigger a compaction manually with the run sub-command:

vast compaction run <rule>

The compaction plugin needs to be loaded both by the client and the server process to use the vast compaction subcommand.

For an overview of the current status of the compaction plugin, you can use the vast status subcommand:

vast status | jq .compaction