As the adoption of Kubernetes (K8s) continues to rise, the necessity for efficient management tools for software running on this platform also increases. For users of Aerospike on Kubernetes, this need is addressed by the Aerospike Kubernetes Operator Control (akoctl) tool. This command-line utility offers simplified and streamlined interactions with the Aerospike Kubernetes Operator, providing essential functions that ease the day-to-day operations of Aerospike clusters within a Kubernetes environment.
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.
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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.
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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.
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Queries, scans, indexes, pagination, and parallelism are common concepts in databases, but each database differs in specifics. It is vital to understand the specifics in order to get the most out of a database. In Aerospike, queries and indexes play a key role in realizing its speed-at-scale objective. The goal of this post is to help developers better understand the Aerospike capabilities in these areas.
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“Real-time describes various operations or processes that respond to inputs reliably within a specified time interval (Wikipedia).”
Real-time data must be processed soon after it is generated otherwise its value is diminished, and real-time applications must respond within a tight timeframe otherwise the user experience and business results are impaired. It is critical for real-time applications to have reliably fast access to all data, real-time or otherwise.
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The Collection Data Types (CDTs) in Aerospike are List and Map. They offer powerful capabilities to model and access your data for speed-at-scale. A major use of the CDTs is to store and process JSON documents efficiently. In the recent Aerospike Database 6.1 release, secondary index capabilities over the CDTs have been enhanced to make the CDTs even more useful and powerful for JSON documents in addition to other uses.
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SQL is broadly used as a data access language for analytics. Even if you are an application developer, chances are you have used it or at least are familiar with it.
Aerospike has broad support for SQL, enabling you to use SQL to access Aerospike data in multiple ways.
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Aerospike Database and the client API provide a rich set of capabilities that have evolved over more than a decade through an increasing number of mission critical deployments. This post provides a high level view of the Aerospike architecture and API to give developers a broader understanding of its architecture and capabilities, and help them become more productive and effective. This post also points to resources for further exploration of specific areas.
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This post focuses on the use of Collection Data Types (CDTs) for data modeling in Aerospike with a large number of objects. This is Part 2 in the two part series on Data Modeling. You can find the first post here.
Data Modeling is the exercise of mapping application objects onto the model and mechanisms provided by the database for persistence, performance, consistency, and ease of access.
Aerospike Database is purpose built for applications that require predictable sub-millisecond access to billions and trillions of objects and need to store many terabytes and petabytes of data, while keeping the cluster size - and therefore the operational costs - small. The goals of large data size and small cluster size mean the capacity of high-speed data storage on each node must be high.