Known limitations for AQL queries

Complexity limitations

The following hard-coded limitations exist for AQL queries:

• An AQL query cannot use more than 1000 result registers. One result register is needed for every named query variable and for internal/anonymous query variables, e.g. for intermediate results. Subqueries also require result registers.
• An AQL query cannot have more than 4000 execution nodes in its initial query execution plan. This number includes all execution nodes of the initial execution plan, even if some of them could be optimized away later by the query optimizer during plan optimization.
• An AQL query cannot use more than 2048 collections/shards.
  From version 3.10.7 onward, this limit is configurable via the --query.max-collections-per-query startup option.
• Expressions in AQL queries cannot have a nesting of more than 500 levels. As an example, the expression 1 + 2 + 3 + 4 is 3 levels deep (because it is interpreted and executed as 1 + (2 + (3 + 4))).
• When reading any data from JSON or VelocyPack input or when serializing any data to JSON or VelocyPack, there is a maximum recursion depth for nested arrays and objects, which is slightly below 200. Arrays or objects with higher nesting than this cause Too deep nesting in Array/Object exceptions.

Please note that even queries that are still below these limits may not yield good performance, especially when they have to put together data from lots of different collections. Please also consider that large queries (in terms of intermediate result size or final result size) can use considerable amounts of memory and may hit the configurable memory limits for AQL queries.

Design limitations

The following design limitations are known for AQL queries:

• Up to v3.12.0, subqueries used inside expressions are pulled out of these expressions and executed beforehand. That means that subqueries do not participate in lazy evaluation of operands, for example in the ternary operator or when used as sub-expressions that are combined with logical AND or OR.

  From v3.12.1 onward, short-circuiting is applied.

  Also see evaluation of subqueries.

• It is not possible to use a collection in a read operation after it was used for a write operation in the same AQL query.

• In the cluster, all collections that are accessed dynamically by traversals working with collection sets (instead of named graphs) must be stated in the query’s initial WITH statement. To make the WITH statement required in single server as well (e.g. for testing a migration to cluster), please start the server with the option --query.require-with.

Storage engine properties

Data of ongoing transactions is stored in RAM. Transactions that get too big (in terms of number of operations involved or the total size of data created or modified by the transaction) are committed automatically. Effectively, this means that big user transactions are split into multiple smaller RocksDB transactions that are committed individually. The entire user transaction does not necessarily have ACID properties in this case.

The following startup options can be used to control the RAM usage and automatic intermediate commits for the RocksDB engine:

• --rocksdb.max-transaction-size

  Transaction size limit (in bytes). Transactions store all keys and values in RAM, so large transactions run the risk of causing out-of-memory situations. This setting allows you to ensure that does not happen by limiting the size of any individual transaction. Transactions whose operations would consume more RAM than this threshold value will abort automatically with error 32 (“resource limit exceeded”).

• --rocksdb.intermediate-commit-size

  If the size of all operations in a transaction reaches this threshold, the transaction is committed automatically and a new transaction is started. The value is specified in bytes.

• --rocksdb.intermediate-commit-count

  If the number of operations in a transaction reaches this value, the transaction is committed automatically and a new transaction is started.

The above values can also be adjusted per query, for example, by setting the following attributes in the call to db._query() in the JavaScript API:

• maxTransactionSize: transaction size limit in bytes
• intermediateCommitSize: maximum total size of operations after which an intermediate commit is performed automatically
• intermediateCommitCount: maximum number of operations after which an intermediate commit is performed automatically