The evaluationStrategy SPARQL query hint - Amazon Neptune
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The evaluationStrategy SPARQL query hint

The evaluationStrategy query hint tells the Amazon Neptune query engine that the fragment of the query annotated should be evaluated from the bottom up, as an independent unit. This means that no solutions from previous evaluation steps are used to compute the query fragment. The query fragment is evaluated as a standalone unit, and its produced solutions are joined with the remainder of the query after it is computed.

Using the evaluationStrategy query hint implies a blocking (non-pipelined) query plan, meaning that the solutions of the fragment annotated with the query hint are materialized and buffered in main memory. Using this query hint might significantly increase the amount of main memory needed to evaluate the query, especially if the annotated query fragment computes a large number of results.

evaluationStrategy SPARQL hint syntax

The evaluationStrategy query hint is specified as a triple pattern included in a SPARQL query.

For clarity, the following syntax uses a hint prefix defined and included in the query to specify the Neptune query-hint namespace:

PREFIX hint: <> hint:SubQuery hint:evaluationStrategy "BottomUp" .
Available Scopes
  • hint:SubQuery


This query hint is supported only in nested subqueries.

For more information about query hint scopes, see Scope of SPARQL query hints in Neptune.

evaluationStrategy SPARQL hint example

This section shows a query written with and without the evaluationStrategy query hint and related optimizations.

For this example, assume that the dataset has the following characteristics:

  • It contains 1,000 edges labeled :connectedTo.

  • Each component node is connected to an average of 100 other component nodes.

  • The typical number of four-hop cyclical connections between nodes is around 100.

No Query Hint

The following SPARQL query extracts all component nodes that are cyclically connected to each other via four hops:

PREFIX : <> PREFIX hint: <> SELECT * { ?component1 :connectedTo ?component2 . ?component2 :connectedTo ?component3 . ?component3 :connectedTo ?component4 . ?component4 :connectedTo ?component1 . }

The approach of the Neptune query engine is to evaluate this query using the following steps:

  • Extract all 1,000 connectedTo edges in the graph.

  • Expand by 100x (the number of outgoing connectedTo edges from component2).

    Intermediate results: 100,000 nodes.

  • Expand by 100x (the number of outgoing connectedTo edges from component3).

    Intermediate results: 10,000,000 nodes.

  • Scan the 10,000,000 nodes for the cycle close.

This results in a streaming query plan, which has a constant amount of main memory.

Query Hint and Subqueries

You might want to trade off main memory space for accelerated computation. By rewriting the query using an evaluationStrategy query hint, you can force the engine to compute a join between two smaller, materialized subsets.

PREFIX : <> PREFIX hint: <> SELECT * { { SELECT * WHERE { hint:SubQuery hint:evaluationStrategy "BottomUp" . ?component1 :connectedTo ?component2 . ?component2 :connectedTo ?component3 . } } { SELECT * WHERE { hint:SubQuery hint:evaluationStrategy "BottomUp" . ?component3 :connectedTo ?component4 . ?component4 :connectedTo ?component1 . } } }

Instead of evaluating the triple patterns in sequence while iteratively using results from the previous triple pattern as input for the upcoming patterns, the evaluationStrategy hint causes the two subqueries to be evaluated independently. Both subqueries produce 100,000 nodes for intermediate results, which are then joined together to form the final output.

In particular, when you run Neptune on the larger instance types, temporarily storing these two 100,000 subsets in main memory increases memory usage in return for significantly speeding up evaluation.