Vespa Serving Tuning

This document describes tuning certain features of an application for high performance, the main focus is on content cluster search features, see Container tuning for tuning of container clusters. The search sizing guide is about scaling an application deployment.

Attribute v.s index

The attribute documentation summaries when to use attribute in the indexing statement. Also see the procedure for changing from attribute to index and vice-versa.

field timestamp type long {
    indexing: summary | attribute
    rank:     filter

If both index and attribute is configured for string type fields, Vespa will do search and matching against the index with default match text. All numeric type fields and tensor fields are attribute (in-memory) fields in Vespa.

By default Vespa does not build any posting list index structures over attribute fields. Adding fast-search to the attribute definition as shown below will add an in-memory B-tree posting list structure which enables faster search for some cases (but not all, see next paragraph):

field timestamp type long {
    indexing:  summary | attribute
    attribute: fast-search
    rank:      filter

When Vespa executes a query with multiple query items it builds a query execution plan which it tries to optimize so that query tree items that are restrictive, meaning they match few documents, are evaluated early so that the temporary result set becomes as low as possible. To do so the query execution plan looks at hit count estimates for each part of the query tree using the index and B-tree dictionaries which track the number of documents a given term occurs in. However, for attribute fields without fast-search there is no hit count estimate so the estimate becomes equal to the total number of documents (matches all) and is thus moved to the end of the query evaluation. A query with only one query term searching a attribute field without fast-search would be a linear scan over all documents and thus very slow:

select * from sources * where range(timestamp, 0, 100);
But if this query term is and-ed with another term which matches fewer documents, that term will determine the cost instead, and fast-search won't be necessary, for example:
select * from sources * where range(timestamp, 0, 100) and uuid contains "123e4567-e89b-12d3-a456-426655440000";
The general rules of thumb for when to use fast-search for an attribute field is:
  • Use fast-search if the attribute field is searched without any other query terms
  • Use fast-search if the attribute field could limit the total number of hits efficiently
Changing fast-search aspect of the attribute is a live change which does not require any re-feeding, so testing the performance with and without is low effort. Adding or removing fast-search requires restart.

Hybrid TAAT and DAAT query evaluation

Vespa supports hybrid query evaluation over inverted indexes combining TAAT and DAAT evaluation to combine the best of both query evaluation techniques. Hybrid is not enabled per default and is triggered by a run time query parameter.

  • TAAT Term At A Time scores documents one query term at a time. The entire posting iterator can be read per query term and the score of a document is accumulated. It is cpu cache friendly as posting data is read sequentially without random seeking the posting list iterator. The downside is that TAAT limits the term based ranking function to be a linear sum of term scores. This downside is one reason why most search engines uses DAAT.
  • DAAT Document At A Time scores documents completely one at a time. This requires multiple seeks in the term posting lists which is cpu cache unfriendly but allows non-linear ranking functions.
Generally Vespa does DAAT (document-at-a-time) query evaluation and not TAAT (term-at-a time) for the reason listed above. Ranking (score calculation) and matching (Does the document match the query logic) is not fully two separate disjunct phases where one first find matches and in a later phase calculates the ranking score. Matching and first-phase score calculation is interleaved when using DAAT.

The first-phase ranking score is assigned to the hit when it satisfies the query constraints and at that point the term iterators are positioned at the document id and one can unpack additional data from the term posting lists, for example for term proximity scoring used by nativeRank ranking feature which also requires unpacking of positions of the term within the document.

The way hybrid evaluation is done is that TAAT is used for sub branches of the overall query tree which is not used for term based ranking. Using TAAT can speed up query matching significantly (up to 20-30%) in cases where the query tree is large and complex and where only parts of the query tree is used for term based ranking. Examples of branches that would require DAAT is using text ranking features like bm25/nativeRank. The list of ranking features which can handle TAAT is long but using attribute or tensor features only can have the entire tree evaluated using TAAT.

For example, for a query where there is a user text query from an end user, one can use userQuery() YQL syntax and combine it with application level constraints. The application level filter constraints in the query could benefit from using TAAT. Given the following document schema:

search news {
  document news {
    field title type string {}
    field body type string{}
    field popularity type float {} 
    field market type string {
    field language type string {
  fieldset default {
    fields: title,body
  rank-profile text-and-popularity {
    first-phase {
      expression: attribute(popularity) + log10(bm25(title)) + log10(bm25(body))

In this case the ranking profile only uses two ranking features, the popularity attribute and the bm25 score of the userQuery() terms against the default fieldset containing the title and body. Notice how market or language is not used in the ranking expression.

In this query example there is a language constraint and a market constraint where both language and market is searched with a long list of valid values using OR, meaning that the document should match any of the market constraints and any of the language constraints.

  'hits': 10,
  'ranking.profile': text-and-popularity",
  'yql': 'select * from sources * where userQuery() and \
   (language contains "en" or language contains "br") and \
   (market contains "us" or market contains "eu" or market contains "apac" or market contains ".." ... ..... ..);'
  'query': 'cat video'

The language and the market constraints in the query tree are not used in the ranking score and that part of the query tree could be evaluated using TAAT. See also multi lookup set filter for how to most efficiently search with large set filters where we want at least one of the constraints to match. The sub-tree result is then passed as a bit vector into the DAAT query evaluation which could speed up the overall evaluation.

Enabling hybrid TAAT is done by passing ranking.matching.termwiselimit=0.01 as a request parameter. One can evaluate if using the hybrid evaluation improves search performance by adding the above parameter. The limit is compared to the hit fraction estimate of the sub branch of the query tree, if the hit fraction estimate is higher than the limit the termwise evaluation is used to evaluate that sub-branch of the query.

Indexing uuids

When configuring string type fields with index, the default match mode is text. This means Vespa will tokenize the content and index the tokens.

The string representation of an Universally unique identifier (UUID) is 32 hexadecimal (base 16) digits, in five groups, separated by hyphens, in the form 8-4-4-4-12, for a total of 36 characters (32 alphanumeric characters and four hyphens).

Example: Indexing 123e4567-e89b-12d3-a456-426655440000 with the above document definition, Vespa will tokenize this into 5 tokens: [123e4567,e89b,12d3,a456,426655440000], each of which could be matched independently, leading to possible incorrect matches.

To avoid this, change the mode to match: word to treat the entire uuid as one token/word:

field uuid type string {
    indexing: summary | index
    match:    word
    rank:     filter

In addition, configure the uuid as a rank: filter field - the field will then be represented as efficient as possible during search and ranking.

The rank:filter behavior can also be triggered at query time on a per query item basis by the in a custom searcher.

Parent child and search performance

When searching imported attribute fields from parent document types there is an additional cost penalty which can be reduced significantly if the imported field is defined with rank:filter and visibility-delay is configured to > 0.

Ranking and ML Model inferences

Vespa scales with the number of hits the query retrieves per node/search thread, and which needs to be evaluated by the first-phase ranking function. Read more on phased ranking. Using phased ranking enables spending more resources during a second phase ranking step than in the first-phase. The first-phase should be focused on getting decent recall (retrieve relevant documents in the top k), while second phase is used to tune precision at k, bringing the relevant documents to the top of the result set.

For text ranking applications, consider using the WAND query operator - WAND can efficiently (sub-linear) find the top k documents using a inner scoring function.

Multi Lookup - Set filtering

Several real world search use cases are built around limiting or filtering based on a set filter. If the contents of a field in the document matches any of the values in the query set it should be retrieved. For example searching data for a set of users:

select * from sources * where user_id = 1 or user_id = 2 or user_id = 3 or user_id = 3 or user_id = 4 or user_id 5 .....;
For OR filters over the same field it is strongly recommended to use the weighted set query operator. It has considerably less overhead than plain OR for set filtering.
select * from sources * where weightedSet(user_id, {"1":1, "2":1, "3":1});
Attribute fields used like the above without other stronger search criteria should be created with fast-search. If there is a large number of unique values in the field it is faster to use hash dictionary instead of btree which is the default data structure for dictionaries for attribute fields with fast-search:
field user_id type long {
  indexing: summary | attribute

E.g if we have 10M unique user_ids in the dictionary and we search for 1000 users per query, btree dictionary would be 1000 lookup times log(10M), while hash based would be 1000 lookups times 1. The weightedSet query set filtering also work brilliantly in combination with TAAT, see hybrid TAAT/DAAT section. Also see dictionary schema reference.

Document summaries - hits

If queries request many hits from a cluster with few content nodes, increasing the summary cache might reduce latency and cost.

Using Explicit Document summaries, Vespa can support memory-only summary/hit fetching if fields referenced in document summary are all defined as attribute.

Boolean, numeric, text attribute

When selecting attribute field type, considering performance, this is a rule of thumb:

  1. Use boolean if a field is a boolean (max two values)
  2. Use a string attribute if there is a set of values - only unique strings are stored
  3. Use a numeric attribute for range searches
  4. Use a numeric attribute if the data is really numeric, don't replace numeric with string numeric
Refer to attributes for details.