Aerospike Developer Blog

Aerospike is known for incredible speed and scalability. As a bonus, people using Aerospike often recognize a far lower Total Cost of Ownership (TCO) compared with other technologies. Optimizing the distribution of data between servers contributes to this low TCO and Aerospike's uniform balance feature allows for almost-perfect even distribution of data across the servers, resulting in better resource utilization and easier capacity planning. This blog post examines how this feature works.

This article describes how queries in Aerospike allow applications to process query results as a stream of records, paginate over results, partition a query for parallelism, and resume execution at a later time.

Spring Data Aerospike now supports the latest version of Spring Data 3 for mapping of Java objects to the Aerospike database. Java developers can now leverage the power, speed and flexibility of the Aerospike database without having to write boilerplate code to transform their object to the database.

AeroLab simplifies the process of installing Aerospike clusters and clients for development, testing, and lab use on either Docker or in AWS. A rich feature set simulates network faults, quickly inserts and updates test data, partitions disks, and more.

Welcome to the Aerospike Standup. Here are some highlights:

Check out the initial release of Aerospike Connect for Elasticsearch, which enables full text search on data in Aerospike. It transfers the configured data to an Elasticsearch cluster using XDR change notifications.

New hotfix releases to Aerospike Database 6.2, 6.1, 6.0, and 5.7 are available. Follow the link in the Releases section below for details.

In this article we focus on establishing connectivity between 2 Aerospike clusters. The goal is to use Aerospike's Cross Data Center Replication feature ( XDR ) to seamlessly send data from a source cluster to a destination cluster. The source cluster needs network visibility of all Aerospike service ports in the remote cluster, and this can present problems, particularly in a Kubernetes environment. Placing a proxy server in front of the private Kubernetes destination cluster can overcome this problem and achieve the desired goal. To demonstrate the solution we start by installing the Kubernetes Operator that will schedule our source and destination databases. In this example, we set up our replication in one direction. Aerospike is capable of supporting 'master/master' replication and provides a conflict resolution mechanism in the event of update clashes. This too could be supported using the XDR proxy.

While it is possible to process a data set using a large number of parallel streams, a higher degree of parallelism may not be necessarily optimal or even possible. This article explores how to think about parallelism, and discusses many bottlenecks that limit the level of parallelism. It also highlights the need to perform measurements in the target setup due to many factors that cannot be easily quantified.

Welcome to the Aerospike Standup. Here are some highlights:

New hotfix releases to Aerospike Database 6.2, 6.1, 6.0, and 5.7 are available. Follow the link in the Releases section for details. Note the Aerospike REST Client is now renamed more accurately as Aerospike REST Gateway.

Aerospike provides several mechanisms for accessing large data sets over parallel streams to match worker throughput in parallel computations. This article explains the key mechanisms, and describes specific schemes for defining data splits and a framework for testing them.

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In this article we focus on side-by-side block and filesystem requests using Kubernetes.​ The driver for this is it will allow us to deploy Aerospike using Aerospike's all flash mode.

Storing database values on a raw block device with index information on file can bring significant cost reductions, especially when considering use cases for Aerospike’s All-Flash. To support such a workload, you can configure Aerospike to use NVMe Flash drives as the primary index store.