![]() If you want to run the query and see how it performs, you prefix it with PROFILE.īelow is the longest Neo4j query plan we’ve ever seen - approximately 230 lines of Cypher - posted in Stack Overflow: EXPLAIN won’t run the query, but will instead provide you with the predicted costs. You can include two keywords that prefix your queries: EXPLAIN and PROFILE. Let’s imagine you’re in your Neo4j Browser with your query, and you’d like to pull up one of the plans explained by Petra. ![]() Mark Needham: How do you actually interact with that plan to make any changes that will make it faster? In version 2.3 we’re using iterative dynamic programming (IDP), which is exhaustive and provides a cheaper logical plan and takes as much or less time than the greedy algorithm. ![]() In 2.2, we were using a greedy algorithm which guaranteed that although we wouldn’t choose the cheapest logical plan, it would not be the most expensive logical plan. More resources on Cypher query executions can be found here and here. We then create an execution plan based on that logical plan, and query execution follows from that. Next we employ an algorithm to select the cheapest one, which is then chosen and optimized. Imagine now we’ve got a plethora of logical plans that we’ve built over different costs. We also estimate our cardinality, which is the number of rows returned after each operation, and which we want to keep low for the same reason. To keep a query running quickly, you want to ensure high selectivity because it will return fewer rows. Using the data from our statistics store, we get the label and the index selectivity. Next, we optimize and normalize the abstract syntax tree and perform functions such as moving all labels and types from the MATCH clause into the WHERE clause, or converting equality statements such as name=Ann into an In.įrom the abstract syntax tree we create a query graph, which is much easier to operate, and then create a series of logical plans. We do this because we always want the cost-based planner to perform better than the rule.īelow is a basic bird’s-eye view of how a query is executed in Cypher:Īfter we get the input query, we tokenize it and then abstract the syntax text tree to do semantic checking, which is basic error checking. The Cypher team tries to benchmark as many queries (and types of queries) as we can with as many datasets as we can. Additionally, if the speed of queries run with a rule are much faster than than those run with the cost-based planner, please let us know so that we can benchmark it. If you do find that the cost-based planner doesn’t work in one patch release, retest it with a new patch release. However, if you need to use the rule-based planner, you can prepend a query with CYPHER planner = rule on a query-by-query basis and set to RULE. All read-only queries use this by default because it generally performs much better than rule-based planners. Using a statistics service, we store information such as the number of movie labels or certain relationship types, and using those statistics we assign costs to various execution plans, which allows us to pick the cheapest one. With the inception of Neo4j 2.2, we introduced a new cost-based planner that is based on database knowledge principles relational databases have been using for decades. The inception of Cypher included a rule planner consisting of rules that used indexes to produce query execution plans. Petra Selmer: I’m going to cover a little about query planners, a brief overview of how Cypher actually executes queries, leading into query plans and some definitions and finally getting to the good stuff which is optimization tips, which hopefully you’ll find useful. When writing a query, be sure that it doesn’t match any cycles, or you can experience unpleasant surprises.Īssume the following sample graph and simple query: CREATE ( a: Node ) WHERE length(nodes(p)) = length(’s Note: Last October at GraphConnect San Francisco, Petra Selmer and Mark Needham – Engineers at Neo Technology – delivered this presentation on how to develop effective queries with Cypher.įor more videos from GraphConnect SF and to register for GraphConnect Europe, check out. ![]() There is one common performance issue our clients run into when trying their first Cypher queries on a dataset in Neo4j.
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