Like a well-prepared community bracing for the impact of a natural disaster, databases must be equipped to face unforeseen challenges. In the world of databases, few things are as dreaded as running out of memory, a situation referred to as OOM. Enter Aerospike's savior: the stop-writes condition, a protective measure designed to avert the nightmarish scenario of OOM. It acts as a vigilant sentinel, ensuring your database stays far from the brink of unrecoverable states. Think of it as your emergency go-bag or the life vest under your seat, offering respite when you need it most, though you hope never to use it.
In June of this year, Aerospike put graph developers front and center with the announcement of a new, developer-ready, real-time scalable graph database. Aerospike Graph is a massively scalable, high-performance graph database that simplifies building and deploying applications for highly connected enterprise-scale datasets.
YAML Schema Suggestions!
Aerospike's configuration format can be difficult to learn and lacks the ecosystem of more standard configuration formats. Using the asconfig tool, new in Aerospike tools 8.3.0, you can write an Aerospike configuration in a standard YAML format with schema validation and IDE suggestions.
In this blog post, I’ll cover new features and changes in the generally available (GA) release of Aerospike 6.4.This release concludes our secondary index storage offerings with the introduction of secondary index on flash.
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Aerospike is renowned as a very fast, very scalable database capable of storing billions or trillions of records, as well as being able to replicate the data to multipe remote database clusters. Hence, a common question which arises is: "How can I validate that two clusters are in sync?". This used to be a difficult problem, but new API calls in Aerospike v5.6 make this task substantially easier. In this blog we will look at one of these new API calls and use it to develop some code to show how a cluster comparator could be written.
This blog explores the new Aerospike driver for LINQPad and its available features. Aerospike for LINQPad 7 is a data context dynamic driver for interactively querying and updating an Aerospike database using "LINQPad". LINQPad is a Graphical Development Tool designed for rapid prototyping, data modeling, data mining, drag-and-drop execution, interactive debugging/testing, quickly learn new frameworks/APIs (e.g., Aerospike API ), etc. The Aerospike driver for LINQPad is designed to support all LINQPad features.
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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.
Source: Photo by Clark Van Der Beken Unsplash
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.
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.
Source: Photo by Clem Onojeghuo on Unsplash
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.