Documents

Vespa models data as documents. A document has a string identifier, set by the application, unique across all documents. A document is a set of key-value pairs. A document has a schema (i.e. type), defined in the schema.

When configuring clusters, a documents element set what document types a cluster is to store. This configuration is used to configure the garbage collector if it is enabled. Additionally, it is used to define default routes for documents sent into the application. By default, a document will be sent to all clusters having the document type defined. Refer to routing for details.

Vespa uses the document ID to distribute documents to nodes. From the document identifier, the content layer calculates a numeric location. A bucket contains all the documents, where a given amount of least-significant bits of the location are all equal. This property is used to enable co-localized storage of documents - read more in buckets and content cluster elasticity.

Documents can be global, see parent/child.

Document IDs

The document identifiers are URIs, represented by a string, which must conform to a defined URI scheme for document identifiers. The document identifier string may only contain text characters, as defined by isTextCharacter in com.yahoo.text.Text.

id scheme

Vespa currently has only one defined scheme, the id scheme: id:<namespace>:<document-type>:<key/value-pair>:<user-specified>

Find examples in the /document/v1/ guide.

Document IDs in search results

The full Document ID (as a string) will often contain redundant information and be quite long; a typical value may look like "id:mynamespace:mydoctype::user-specified-identifier" where only the last part is useful outside Vespa. The Document ID is therefore not stored in memory, and it not always present in search results. It is therefore recommended to put your own unique identifier (usually the "user-specified-identifier" above) in a document field, typically named "myid" or "shortid" or similar:

field shortid type string {
    indexing: attribute | summary
}

This enables using a document-summary with only in-memory fields while still getting the identifier you actually care about. If the "user-specified-identifier" is just a simple number you could even use "type int" for this field for minimal memory overhead.

Namespace

The namespace in document ids is useful when you have multiple document collections that you want to be sure never end up with the same document id. It has no function in Vespa beyond this, and can just be set to any short constant value like for example "doc". Consider also letting synthetic documents used for testing use namespace "test" so it's easy to detect and remove them if they are present outside the test by mistake.

Example - if feeding

• document A by curl -X POST https:.../document/v1/first_namespace/my_doc_type/docid/shakespeare
• document B by curl -X POST https:.../document/v1/second_namespace/my_doc_type/docid/shakespeare

then those will be separate documents, both searchable, with different document IDs. The document ID differs not in the user specified part (this is shakespeare for both documents), but in the namespace part (first_namespace vs second_namespace). The full document ID for document A is id:first_namespace:my_doc_type::shakespeare.

The namespace has no relation to other configuration elsewhere, like in services.xml or in schemas. It is just like the user specified part of each document ID in that sense. Namespace can not be used in queries, other than as part of the full document ID. However, it can be used for document selection, where id.namespace can be accessed and compared to a given string, for instance. An example use case is visiting a subset of documents.

Fields

Documents can have fields, see the schema reference.

A field can not be defined with a default value. Use a document processor to assign a default to document put/update operations.

Fieldsets

Use fieldset to limit the fields that are returned from a read operation, like get or visit - see examples. Vespa may return more fields than specified if this does not impact performance.

There are two options for specifying a fieldset:

• Built-in fieldset
• Name of a document type, then a colon ":", followed by a comma-separated list of fields (for example music:artist,song to fetch two fields declared in music.sd)

Built-in fieldsets:

If a built-in field set is not used, a list of fields can be specified. Syntax:

<document type>:field1,field2,…

Example:

music:title,artist

Document expiry

To auto-expire documents, use a selection with now. Example, set time-to-live (TTL) for music-documents to one day, using a field called timestamp:

<documents garbage-collection="true">
    <document type="music" selection="music.timestamp &gt; now() - 86400" />
</documents>

Note: The selection expression says which documents to keep, not which ones to delete. The timestamp field must have a value in seconds since EPOCH:

field timestamp type long {
    indexing: attribute
    attribute {
        fast-access
    }
}

When garbage-collection="true", Vespa iterates over the document space to purge expired documents. Vespa will invoke the configured GC selection for each stored document at most once every garbage-collection-interval seconds.

• Using a selection with now can have side effects when re-feeding or re-processing documents, as timestamps can be stale. A common problem is feeding with too old timestamps, resulting in no documents being indexed.
• Normally, documents that are already expired at write time are not persisted. When using create (Create if nonexistent), it is possible to create documents that are expired and will be removed in next cycle.
• Deploying a configuration where the selection string selects no documents will cause all documents to be garbage collected. Use visit to test the selection string. Garbage collected documents are not to be expected to be recoverable.
• The fields that are referenced in the selection expression should be attributes. Also, either the fields should be set with "fast-access" or the number of searchable copies in the content cluster should be the same as the redundancy. Otherwise, the document selection maintenance will be slow and have a major performance impact on the system.
• Imported fields can be used in the selection string to expire documents, but special care needs to be taken when using these. See using imported fields in selections for more information and restrictions.
• Document garbage collection is a low priority background operation that runs continuously unless preempted by higher priority operations. If the cluster is too heavily loaded by client feed operations, there's a risk of starving GC from running. To verify that garbage collection is not starved, check the vds.idealstate.max_observed_time_since_last_gc_sec.average distributor metric. If it significantly exceeds garbage-collection-interval it is an indication that GC is starved.

To batch remove, set a selection that matches no documents, like "not music"

Use vespa visit to test the selection. Dump the IDs of all documents that would be preserved:

$ vespa visit --selection 'music.timestamp > now() - 86400' --field-set "music.timestamp"

Negate the expression by wrapping it in a not to dump the IDs of all the documents that would be removed during GC:

$ vespa visit --selection 'not (music.timestamp > now() - 86400)' --field-set "music.timestamp"

Processing documents

To process documents, use Document processing. Examples are enriching documents (look up data from other sources), transform content (like linguistic transformations, tokenization), filter data and trigger other events based on the input data.

See the sample app album-recommendation-docproc for use of Vespa APIs like:

• Document API - work on documents and fields in documents, and create unit tests using the Application framework
• Document Processing - chain independent processors with ordering constraints

The sample app vespa-documentation-search has examples of processing PUTs or UPDATEs (using create-if-nonexistent) of documents in OutLinksDocumentProcessor. It is also in introduction to using multivalued fields like arrays, maps and tensors. Use the VespaDocSystemTest to build code that feeds and tests an instance in the Vespa Developer Cloud / local Docker instance.

Both sample apps also use the Document API to GET/PUT/UPDATE other documents as part of processing, using asynchronous DocumentAccess. Use this as a starting point for applications that enrich data when writing.