Data retrieval performance optimizations in ArangoDB 3.3.9

00General, ReleasesTags:

Our recent release 3.3.9 includes several performance optimizations for data retrieval cases. Benefits can be expected for both storage engines, MMFiles and RocksDB, AQL batch lookup queries, and cluster AQL queries.

MMFiles index batch lookups

For the MMFiles engine, an optimization has been made for retrieving multiple documents from an index (hash index, skiplist index or persistent index) in a batch.
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An implementation of phase-fair reader/writer locks

00GeneralTags:

We were in search for some C++ reader/writer locks implementation that allows a thread to acquire a lock and then optionally pass it on to another thread. The C++11 and C++14 standard library lock implementations std::mutex and shared_mutex do not allow that (it would be undefined behaviour – by the way, it’s also undefined behaviour when doing this with the pthreads library).

Additionally, we were looking for locks that would neither prefer readers nor writers, so that there will be neither reader starvation nor writer starvation. And then, we wanted concurrently queued read and write requests that compete for the lock to be brought into some defined execution order. Ideally, queued operations that cannot instantly acquire the lock should be processed in approximately the same order in which they queued. Read more

Configuring ArangoDB-PHP to use active failover

00Drivers, General, PHPTags: , ,

This article is about setting up active failover for ArangoDB-PHP, the PHP client driver for ArangoDB. It requires ArangoDB-PHP 3.3.2 or higher, and an ArangoDB server version of 3.3.4 or higher.

Active failover: basic setup

Historically, ArangoDB-PHP has been able to connect to a single ArangoDB endpoint, i.e. one combination of IP address and port number.

To connect to an ArangoDB server that is running on localhost or on a remote server, simply set the OPTION_ENDPOINT item in the ConnectionOptions and connect: Read more

Index types and how indexes are used in ArangoDB: Part II

00GeneralTags: , ,

In the first part of this article we dived deep into what indexes are currently available in ArangoDB (3.2 and 3.3), also briefly looking at what improvements are coming with ArangoDB 3.4. Read Part I here.

In this Part II, we are going to focus on how to actually add indexes to a data model and speed up specific queries.

Adding indexes to the data model

The goal of adding an extra index to the data model is to speed up a certain query or even multiple queries.

One of the first things that should be done during development of AQL queries should be to review the output of the explain command. A query can be explained using ArangoDB’s WEB UI or from the ArangoShell. In the ArangoShell it is as simple as db._explain(query), where query is the AQL query string. To explain a query which also has bind parameters, they need to be passed separately into the command, e.g. db._explain(query, bindParameters).
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Index types and how indexes are used in ArangoDB: Part I

00GeneralTags: , ,

As in other database systems, indexes can be used in ArangoDB to speed up data retrieval queries, sometimes by many orders of magnitude. Getting the indexes set up the right way is essential for good query performance, so this is an important topic that affects most ArangoDB installations.

This is Part I of how indexes are used by ArangoDB where we discuss what types of indexes are available in the database. In Part II, we will dig deeper into how to actually add indexes to a data model and speed up specific queries. Read Part II here. Read more

Killing a long-running query

01General, Query LanguageTags: ,

Suppose there is an AQL query that’s executing in the server for a long time already and you want to get rid of it. What can be done to abort that query?

If a connection to the server can still be established, the easiest is to use the ArangoShell to fetch the list of currently executing AQL queries and send a kill command to the server for the correct query. Read more

AQL optimizer improvements for 2.8

00PerformanceTags: , ,

With the 2.8 beta phase coming to an end it’s time to shed some light on the improvements in the 2.8 AQL optimizer. This blog post summarizes a few of them, focusing on the query optimizer. There’ll be a follow-up post that will explain dedicated new AQL features soon. Read more

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