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ArangoDB Boosts Multi-Model Database Performance with Release of ArangoDB 3.6

New features bring the high-availability of a cluster combined with the performance of a single instance, as well as query optimizations that see up to 30x performance improvements

San Francisco and Cologne, Germany – January 8, 2020 – ArangoDB, the leading open source native multi-model database, today announced the GA release of ArangoDB 3.6. ArangoDB 3.6 introduces OneShard, the ability to restrict individual databases to one node in a cluster, to ArangoDB’s Enterprise offering, and also includes major performance improvements that increase query speeds up to 30x faster.

A database created with OneShard enabled is bound to a single database server node, but still replicated synchronously to additional nodes. This ensures the high-availability and fault tolerance of a cluster setup and performance similar to a single instance, as well as the possibility to run transactions with ACID guarantees. OneShard is ideal for use cases with graph traversals and JOIN-heavy queries, as well as multi-tenant applications.

“In conversations with our community, we found many of our users expressed the need for the high-availability and fault-tolerant benefits of a cluster, but they didn’t necessarily want to scale horizontally and sacrifice performance,” said Claudius Weinberger, CEO and co-founder of ArangoDB. “With the release of ArangoDB 3.6, we are pleased to offer developers a solution with OneShard, as well as a plethora of additional performance improvements.”

The additional features in ArangoDB 3.6, included in both the Community and Enterprise editions, are:

Subquery performance optimization: 30x faster query execution time
With the introduction of a new optimizer rule called splice-subqueries, subquery splicing inlines the execution of certain subqueries, yielding up to 30x faster query execution time.

Parallel execution of AQL queries: Increase cluster AQL query speed by 40%
ArangoDB 3.6 includes the ability to parallelize work in many cluster ArangoDB Query Language (AQL) queries when there are multiple database servers involved, increasing speedups of the queries by up to 40%.

Late document materialization: Accelerate SORT and LIMIT queries by 300%
With the late document materialization optimization, ArangoDB limits sorting to index data for queries that use a combination of SORT and LIMIT, reducing memory usage and better utilizing caches. In performance testing, ArangoDB saw query speedups up to 300%.

Early pruning of non-matching documents: Query improvements up to 50% faster
ArangoDB 3.6 evaluates FILTER conditions on non-index attributes the same time it does a full collection or index scan. With the scanning and filtering happening concurrently, queries that filter on non-index attributes will run faster. In testing ArangoDB saw performance improvements up to 50%.

Additional improvements that increased query speeds up to 50% include UPDATE and REPLACE query optimizations, and faster date calculation operations.

New ArangoSearch capabilities: Support for word-based auto-completion queries and dynamic search expressions
ArangoSearch, ArangoDB’s full-text search engine with ranking capabilities, now offers edge n-grams to support word-based auto-completion queries. In ArangoDB 3.6, ArangoSearch also supports expressions with array comparison operators in AQL and the ability to mark the beginning/end of the input sequence in the n-gram Analyzer. TOKENS() and PHRASE() functions also accept arrays, enabling dynamic search expressions.

ArangoDB 3.6 is available immediately for download here, and is also available on ArangoDB ArangoGraph, ArangoDB’s recently released managed service.

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ArangoDB Accolades
ArangoDB is listed among the top three graph databases, the top ten document stores and ranks in the top seven search engines on db-engines.com. In addition, ArangoDB was selected as the leading graph database for 2020 on G2 Crowd (4.8/5 stars) for the second year in a row and Gartner Peer Insights Customers voted ArangoDB among the best rated operational database management systems with 4.7/5 stars and 86 reviews.


About Arango

Arango delivers the Contextual AI Data Infrastructure that forms a unified System of Context, helping enterprises build AI they can trust, run at scale, and achieve better economics. With Arango, teams get the trusted data foundation needed to deliver explainable, accurate outcomes grounded in real business context, so AI decisions are transparent and reliable. As AI initiatives grow, Arango enables organizations to deploy with confidence, scaling across multimodel data without adding complexity. By unifying graph, vector, document, key-value, and search in a single platform, Arango helps shift resources from integration to innovation — freeing teams to focus on building what matters most.

Trusted by NVIDIA, HPE, the London Stock Exchange, the U.S. Air Force, NIH, Siemens, Synopsys, and Articul8, Arango powers enterprise AI with context, confidence, and scale. Arango is a proud member of the NVIDIA Inception Program and the AWS ISV Accelerate Program. Learn more at arango.ai, LinkedIn, and G2.

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press@arango.ai