Optimizing View Query Performance

You can improve the performance of View queries with a primary sort order, stored values and other optimizations

Primary Sort Order

The index behind an ArangoSearch View can have a primary sort order. A direction can be specified upon View creation for each uniquely named attribute (ascending or descending), to enable an optimization for AQL queries which iterate over a View and sort by one or multiple of the attributes. If the field(s) and the sorting direction(s) match then the data can be read directly from the index without actual sort operation.

You can only set the primarySort option and the related primarySortCompression and primarySortCache options on View creation.

View definition example:

{
  "links": {
    "coll1": {
      "fields": {
        "text": {}
      }
    },
    "coll2": {
      "fields": {
        "text": {}
      }
    },
    "primarySort": [
      {
        "field": "text",
        "direction": "asc"
      }
    ]
  }
}

AQL query example:

FOR doc IN viewName
  SORT doc.name
  RETURN doc

Execution plan without a sorted index being used:

Execution plan:
 Id   NodeType            Est.   Comment
  1   SingletonNode          1   * ROOT
  2   EnumerateViewNode      1     - FOR doc IN viewName   /* view query */
  3   CalculationNode        1       - LET #1 = doc.`val`   /* attribute expression */
  4   SortNode               1       - SORT #1 ASC   /* sorting strategy: standard */
  5   ReturnNode             1       - RETURN doc

Execution plan with a the primary sort order of the index being utilized:

Execution plan:
 Id   NodeType            Est.   Comment
  1   SingletonNode          1   * ROOT
  2   EnumerateViewNode      1     - FOR doc IN viewName SORT doc.`val` ASC   /* view query */
  5   ReturnNode             1       - RETURN doc

To define more than one attribute to sort by, simply add more sub-objects to the primarySort array:

{
  "links": {
    "coll1": {
      "fields": {
        "text": {},
        "date": {}
      }
    },
    "coll2": {
      "fields": {
        "text": {}
      }
    },
    "primarySort": [
      {
        "field": "date",
        "direction": "desc"
      },
      {
        "field": "text",
        "direction": "asc"
      }
    ]
  }
}

The optimization can be applied to View queries which sort by both fields as defined (SORT doc.date DESC, doc.name), but also if they sort in descending order by the date attribute only (SORT doc.date DESC). Queries which sort by text alone (SORT doc.name) are not eligible, because the View is sorted by date first. This is similar to persistent indexes, but inverted sorting directions are not covered by the View index (e.g. SORT doc.date, doc.name DESC).

Note that the primarySort option is immutable: it can not be changed after View creation. It is therefore not possible to configure it through the Web UI. The View needs to be created via the HTTP or JavaScript API (arangosh) to set it.

The primary sort data is LZ4-compressed by default (primarySortCompression is "lz4"). Set it to "none" on View creation to trade space for speed.

You can additionally set the primarySortCache option to true to always cache the primary sort columns in memory, which can improve the query performance:

{
  "links": {
    "coll1": {
      "fields": {
        "text": {},
        "date": {}
      }
    },
    "coll2": {
      "fields": {
        "text": {}
      }
    },
    "primarySort": [
      {
        "field": "date",
        "direction": "desc"
      },
      {
        "field": "text",
        "direction": "asc"
      }
    ],
    "primarySortCache": true
  }
}

See the primarySortCache View property for details.

Stored Values

It is possible to directly store the values of document attributes in View indexes with the View property storedValues (not to be confused with storeValues). You can only set this option on View creation.

View indexes may fully cover SEARCH queries for improved performance. While late document materialization reduces the amount of fetched documents, this optimization can avoid to access the storage engine entirely.

{
  "links": {
    "articles": {
      "fields": {
        "categories": {}
      }
    }
  },
  "primarySort": [
    { "field": "publishedAt", "direction": "desc" }
  ],
  "storedValues": [
    { "fields": [ "title", "categories" ] }
  ]
}

In above View definition, the document attribute categories is indexed for searching, publishedAt is used as primary sort order and title as well as categories are stored in the View using the new storedValues property.

FOR doc IN articlesView
  SEARCH doc.categories == "recipes"
  SORT doc.publishedAt DESC
  RETURN {
    title: doc.title,
    date: doc.publishedAt,
    tags: doc.categories
  }

The query searches for articles which contain a certain tag in the categories array and returns title, date and tags. All three values are stored in the View (publishedAt via primarySort and the two other via storedValues), thus no documents need to be fetched from the storage engine to answer the query. This is shown in the execution plan as a comment to the EnumerateViewNode: /* view query without materialization */

Execution plan:
 Id   NodeType            Est.   Comment
  1   SingletonNode          1   * ROOT
  2   EnumerateViewNode      1     - FOR doc IN articlesView SEARCH (doc.`categories` == "recipes") SORT doc.`publishedAt` DESC LET #1 = doc.`publishedAt` LET #7 = doc.`categories` LET #5 = doc.`title`   /* view query without materialization */
  5   CalculationNode        1       - LET #3 = { "title" : #5, "date" : #1, "tags" : #7 }   /* simple expression */
  6   ReturnNode             1       - RETURN #3

Indexes used:
 none

Optimization rules applied:
 Id   RuleName
  1   move-calculations-up
  2   move-calculations-up-2
  3   handle-arangosearch-views

The stored values data is LZ4-compressed by default ("lz4"). Set it to "none" on View creation to trade space for speed.

{
  "links": {
    "articles": {
      "fields": {
        "categories": {}
      }
    }
  },
  "primarySort": [
    { "field": "publishedAt", "direction": "desc" }
  ],
  "storedValues": [
    { "fields": [ "title", "categories" ], "compression": "none" }
  ]
}

You can additionally enable the ArangoSearch column cache for stored values by setting the cache option in the storedValues definition to true:

{
  "links": {
    "articles": {
      "fields": {
        "categories": {}
      }
    }
  },
  "primarySort": [
    { "field": "publishedAt", "direction": "desc" }
  ],
  "storedValues": [
    { "fields": [ "title", "categories" ], "cache": true }
  ]
}

The stored are always cached in memory with this option enabled, which can improve the query performance.

See the storedValues View property for details.

Condition Optimization Options

The SEARCH operation in AQL accepts an option conditionOptimization to give you control over the search criteria optimization:

FOR doc IN myView
  SEARCH doc.val > 10 AND doc.val > 5 /* more conditions */
  OPTIONS { conditionOptimization: "none" }
  RETURN doc

By default, all conditions get converted into disjunctive normal form (DNF). Numerous optimizations can be applied, like removing redundant or overlapping conditions (such as doc.val > 10 which is included by doc.val > 5). However, converting to DNF and optimizing the conditions can take quite some time even for a low number of nested conditions which produce dozens of conjunctions / disjunctions. It can be faster to just search the index without optimizations.

Also see SEARCH operation.

Count Approximation

The SEARCH operation in AQL accepts an option countApproximate to control how the total count of rows is calculated if the fullCount option is enabled for a query or when a COLLECT WITH COUNT clause is executed.

By default, rows are actually enumerated for a precise count. In some cases, an estimate might be good enough, however. You can set countApproximate to "cost" for a cost based approximation. It does not enumerate rows and returns an approximate result with O(1) complexity. It gives a precise result if the SEARCH condition is empty or if it contains a single term query only (e.g. SEARCH doc.field == "value"), the usual eventual consistency of Views aside.

FOR doc IN viewName
  SEARCH doc.name == "Carol"
  OPTIONS { countApproximate: "cost" }
  COLLECT WITH COUNT INTO count
  RETURN count

Field normalization value caching

Introduced in: v3.9.5

Normalization values are computed for fields which are processed with Analyzers that have the "norm" feature enabled. These values are used to score fairer if the same tokens occur repeatedly, to emphasize these documents less.

You can set the cache option to true for individual View links or fields to always cache the field normalization values in memory. This can improve the performance of scoring and ranking queries:

{
  "links": {
    "coll1": {
      "fields": {
        "categories": {
          "analyzers": ["text_en"],
          "cache": true
        }
      }
    },
    "coll2": {
      "includeAllFields": true,
      "analyzers": ["text_en"],
      "cache": true
    }
  }
}

See the cache Link property for details.

The "norm" Analyzer feature has performance implications even if the cache is used. You can create custom Analyzers without this feature to disable the normalization and improve the performance. Make sure that the result ranking still matches your expectations without normalization. It is recommended to use normalization for a good scoring behavior.

Primary key caching

Introduced in: v3.9.6

You can set the primaryKeyCache View property to true on View creation to always cache the primary key columns in memory. This can improve the performance of queries that return many documents, making it faster to map document IDs in the index to actual documents:

{
  "links": {
    "articles": {
      "fields": {
        "categories": {}
      }
    }
  },
  "primaryKeyCache": true
}

See the primaryKeyCache View property for details.