--- title: "The great AQL shootout: ArangoDB 2.5 vs 2.6 - ArangoDB" description: "For the ArangoDB 2.6 release from last week we’ve put some performance tests together. The tests will compare the AQL query execution times in 2.5 and 2.6. The results look quite promising: 2.6 outperformed 2.5 for all tested queries, mostly by factors of 2 to 5. A few dedicated AQL features in the tests got…" --- ArangoDB 3.12 Product Release Announcement! Read the blog for details. 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The tests will compare the AQL query execution times in 2.5 and 2.6. The results look quite promising: **2.6 outperformed 2.5 for all tested queries**, mostly by factors of 2 to 5. A few dedicated AQL features in the tests got boosted even more, resulting in query execution time reductions of 90 % and more. Finally, the tests also revealed a dedicated case for which 2.6 provides a several hundredfold speedup. Also good news is that not a single of the test queries ran slower in 2.6 than in 2.5. ### What was tested? The tests execute several read-only AQL queries on datasets of different sizes and measure the query execution times. The tests were conducted in both ArangoDB 2.5 and 2.6, so the results of the two ArangoDB versions can be compared. Though the tests do not cover every possible type of AQL operation, feature and function, they still do cover a wide range of features, e.g. lookups, joins, COLLECT operations, sorting, subqueries, and some AQL functions. Overall, the test suite contains 33 different cases. All queries were run on datasets of three different sizes to validate that the results are relevant for datasets of various sizes. The dataset sizes are 10,000 documents, 100,000 documents, and 1,000,000documents. Each query was repeated a few times so outliers in execution time can be identified. There is full disclosure of the test methodology and the test script below, so anyone interested can repeat the tests locally and verify the results. ### Test results The combined test results from 2.5 and 2.6 can be found in this [PDF file](https://jsteemann.github.io//downloads/code/arango-25-26-shootout-results.pdf). There is also an *ods* version of the same file [here](https://jsteemann.github.io/downloads/code/arango-25-26-shootout-results.ods). A description of the columns and test cases used in these files can be found further below. For the detail-loving folks, here are the raw results for both versions in isolation: - [2.5](https://jsteemann.github.io/downloads/code/arango-25-26-shootout-results-25.txt) - [2.6](https://jsteemann.github.io/downloads/code/arango-25-26-shootout-results-26.txt) The results show that ArangoDB 2.6 was consistently faster for **all** AQL queries included in the tests. The queries that improved most in 2.6 over 2.5 include: - `FILTER` conditions: simple `FILTER` conditions as used in the tests are 3 to 5 times faster - simple joins using the primary index (`_key` attribute), hash index or skiplist index are 2 to 3.5 times faster - sorting on a string attribute is 2.5 to 3 times faster - extracting the `_key` or other top-level attributes from documents is 4 to 5 times faster - `COLLECT` statements: simple `COLLECT` statements like the ones in the tests are 7 to 15 times faster - looking up documents using `IN` lists with a substantial amount of values contained in the `IN` list is 250 to 700 times faster The one thing that did not change much when comparing 2.6 with 2.5 is iterating over a collection and returning all its documents unmodified. The speedups observed for this type of query are between 18 and 25 %, which is the lowest speedup measured by the tests. Still 18 to 25 % seem okay as a free take-away. Speedups were observed for all three test dataset sizes alike. In some cases, the speedups varied a bit with the dataset sizes, but it was still in the same ballpark for all three datasets. The conclusion is thus that the speedups did not depend much on the dataset sizes. ### Reasons for speedups There are several reasons why the 2.6 performance is better than in previous versions. The main reason is that we spent much time optimizing some of the critical AQL code paths. Then we also worked on optimizations for specific features, which are used by some of the tested queries. If you’re interested in the details, here they are: - [COLLECTing with a hash table](https://blog.arangodb.com/blog/2015/04/collecting-with-a-hash-table) - [AQL functions improvements](https://blog.arangodb.com/blog/2015/04/aql-functions-improvements) - [Subquery optimizations](https://jsteemann.github.io/blog/2015/05/04/subquery-optimizations/) - [Return value optimization for AQL](https://blog.arangodb.com/blog/2015/05/return-value-optimization-for-aql) - [IN-list improvements](https://blog.arangodb.com/blog/2015/05/in-list-improvements) Additionally, UTF-8 string comparisons were boosted by the upgrade from ICU 52 to ICU 54. The latter version contains a rewritten and much faster UTF-8-aware strcmp, which we heavily rely on. ### Test methodology Each query was run five times on each dataset, so execution time outliers can be identified. The results contain the minimum, maximum and average execution times for each query. Queries were run in isolation on an otherwise idle server. The queries were all run inside the server, so there was no HTTP/network traffic involved for shipping query results (note: this was also [vastly improved in 2.6](https://jsteemann.github.io/blog/2015/04/01/improvements-for-the-cursor-api/) but this is not the subject of this post). All tests were run on my local machine, which has 4 cores, 8 CPUs (though the number of CPUs will not matter for any of the tests), 12 GB of physical memory, a Linux 3.16 kernel and an Ubuntu 15 OS. All datasets fit into the main memory, so tests were not I/O-bound. The ArangoDB versions tested were 2.5.4 and 2.6.0-alpha2. Both versions were hand-compiled with g++ 4.9.1 with options `CC='gcc' CXX='g++' CFLAGS='-O3 -Wall' CXXFLAGS='-O3 -Wall'`. The complete test script, including the setup of the test data, is contained in this file. It can be run inside *arangod* by typing the following in the server console: ``` require("internal").load("/path/to/arango-25-26-shootout-script.js"); ``` Note that this needs an *arangod* started with option `--console`. Also note that running the script will only test the current *arangod* instance, so the script needs to be run once in a 2.5 instance and once in 2.6. Running the script will set up the test collections, run all queries on them (you will need some patience for this) and finally print a table like the following: ``` test name | collection | runs | min (s) | max (s) | avg (s) --------------------------------------------------------------------------------------------------- collect-number | 10k | 5 | 0.0760 | 0.1638 | 0.0943 collect-number | 100k | 5 | 0.8697 | 0.8966 | 0.8803 collect-number | 1000k | 5 | 10.4320 | 10.6597 | 10.5314 collect-string | 10k | 5 | 0.1211 | 0.1319 | 0.1250 collect-string | 100k | 5 | 1.5406 | 1.5974 | 1.5641 collect-string | 1000k | 5 | 19.0708 | 19.0966 | 19.0825 collect-count-number | 10k | 5 | 0.0763 | 0.0792 | 0.0778 ``` These result columns have the following meanings: - *test name*: name of test - *collection*: name of collection. *10k* is a collection with 10,000 documents, *100k* contains 100,000 documents, and *1000k* contains 1,000,000 documents. - *runs*: number of times the query was run - *min (s)*: minimum query execution time (in seconds) - *max (s)*: maximum query execution time (in seconds) - *avg (s)*: average query execution time (in seconds) ### Test data The test datasets for the three collections are filled with artificial data. Test documents are created like this: ``` collection.insert({ _key: "test" + i, value1: i, value2: "test" + i, value3: i, value4: "test" + i, value5: i, value6: "test" + i, value7: i % g, value8: "test" + (i % g) }); ``` Each document has a `_key` attribute and 8 other attributes, `value1` to `value8`. `value1`, `value3`, `value5` and `value7` are numeric attributes, the other attributes contain string values. The attributes `value1` to `value6` contain unique values. The attributes `value7` and `value8` contain repeating values. They are used for `COLLECT` queries. `value1` and `value2` are each indexed with a hash index. `value3` and `value4` are each indexed with a skiplist index. `value5` to `value8` are not indexed. This way queries can be run on the same values, but with different indexes and even without indexes. ### Test cases The test cases cover the following queries: - *collect-number* and *collect-string*: run `COLLECT` on a repeating attribute, which is either numeric or a string - *collect-count-number* and *collect-count-string*: ditto, but also calculate the group lengths using `WITH COUNT INTO` - *subquery*: run a single-document subquery for each document of the original collection - *concat*: for each document in the collection, concat the document `_key` attribute with another document attribute using `CONCAT()` - *merge*: for each document in the collection, merge the document with another object using `MERGE()` - *keep*: for each document in the collection, remove all but a few named attributes from it using `KEEP()` - *unset*: for each document in the collection, remove a few named attributes from it using `UNSET()` - *min-number* and *min-string*: return the minimum value of a specific attribute from all documents in the collection, which is either numeric or a string. This uses `MIN()` - *max-number* and *max-string*: ditto, but using `MAX()` - *sort-number* and *sort-string*: sort all documents in the collection by a non-indexed attribute, which is either numeric or a string - *filter-number* and *filter-string*: filter all documents in the collection using a non-indexed attribute, which is either numeric or a string - *extract-doc*: return all documents in the collection unmodified - *extract-key*: return the `_key` attribute of all documents in the collection - *extract-number* and *extract-string*: return an attribute from all documents in the collection, which is either numeric or a string - *join-key*: for each document in the collection, perform a join on the `_key` attribute on the collection itself (i.e. `FOR c1 IN @@c FOR c2 IN @@c FILTER c1._key == c2._key RETURN c1`) - *join-id*: ditto, but perform the join using the `_id` attribute - *join-hash-number* and *join-hash-string*: ditto, but join using a hash index on a numeric or string attribute - *join-skiplist-number* and *join-skiplist-string*: ditto, but join using a skiplist index on a numeric or string attribute - *lookup-key*, *lookup-hash-number*, *lookup-hash-string*, *lookup-skiplist-number*, *lookup-skiplist-string*: compile an IN-list of 10,000 lookup values and search these 10,000 documents in the collection using either the primary index (`_key` attribute), a hash index or a skiplist index. The latter two are tested on numeric and string attributes. Further implementation details can be checked in the [test script](https://jsteemann.github.io/downloads/code/arango-25-26-shootout-script.js). [Twitter](https://twitter.com/intent/tweet?url=https://blog.arangodb.com/blog/2015/06/the-great-aql-shootout-arangodb-2-5-vs-2-6) [LinkedIn](http://www.linkedin.com/shareArticle?mini=true&url=https://blog.arangodb.com/blog/2015/06/the-great-aql-shootout-arangodb-2-5-vs-2-6) [Reddit](https://www.reddit.com/share?url=https://blog.arangodb.com/blog/2015/06/the-great-aql-shootout-arangodb-2-5-vs-2-6) [Yahoo](https://plus.google.com/share?url=https://blog.arangodb.com/blog/2015/06/the-great-aql-shootout-arangodb-2-5-vs-2-6%3Futm_medium%3Dsocial%26utm_source%3Dgoogle_plus) ![Picture of Jan Steemann](https://blog.arangodb.com/hubfs/Imported_Blog_Media/Authors/Jan-Steemann-100x100.webp) June 30, 2015 , [Jan Steemann](https://blog.arangodb.com/blog/author/jan-steemann) After more than 30 years of playing around with 8 bit computers, assembler and scripting languages, Jan decided to move on to work in database engineering. Jan is now a senior C/C++ developer with the ArangoDB core team, being there from version 0.1. He is mostly working on performance optimization, storage engines and the querying functionality. He also wrote most of AQL (ArangoDB’s query language). ### Leave a Comment Tags - [General](https://blog.arangodb.com/blog/topic/general) - [Release](https://blog.arangodb.com/blog/topic/release) - [Community](https://blog.arangodb.com/blog/topic/community) - [Releases](https://blog.arangodb.com/blog/topic/releases) - [Performance](https://blog.arangodb.com/blog/topic/performance) - [Tutorial](https://blog.arangodb.com/blog/topic/tutorial) - [Foxx](https://blog.arangodb.com/blog/topic/foxx) - [AQL](https://blog.arangodb.com/blog/topic/aql) - [API](https://blog.arangodb.com/blog/topic/api) - [Newsletter](https://blog.arangodb.com/blog/topic/newsletter) - [Cluster](https://blog.arangodb.com/blog/topic/cluster) - [Graph](https://blog.arangodb.com/blog/topic/graph) - [Graphs](https://blog.arangodb.com/blog/topic/graphs) - [Documentation](https://blog.arangodb.com/blog/topic/documentation) - [Javascript](https://blog.arangodb.com/blog/topic/javascript) - [Query Language](https://blog.arangodb.com/blog/topic/query-language) - 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