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Book Review: Developer, Advocate!
Developer, Advocate! is a set of interviews with prominent technologists, covering what drives their interest and enthusiasm in the industry. The brevity of each interview provides direct information and insight that can be read separately at any time, in any order, enabling those with busy schedules to read, put down, and repeat.
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How to Use Redis TimeSeries with Grafana for Real-Time Analytics
In this article, author Roshan Kumar discusses how a purpose-built database like RedisTimeSeries can be used to manage time-series data. He also shows how to visualize this data in a Grafana dashboard.
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Postgres Handles More Than You Think
Thinking about scaling beyond Postgres with a data store like Redis or Elasticsearch? Think again before adopting a complex infrastructure. Postgres can scale for heavy loads and offers powerful features which are not obvious at first sight. For example, it's possible to enable in-memory caching, text search, specialized indexing, and key-value storage. Article
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Understanding Serverless: Tips and Resources for Building Servicefull Applications
There are still many misconceptions and concerns regarding serverless solutions. Vendor lock-in, tooling, cost management, cold starts, monitoring and the development lifecycle are all hot topics where serverless technologies are concerned. This article shares tips and resources to guide serverless newcomers towards building powerful, flexible and cost-effective serverless applications.
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How to Use Open Source Prometheus to Monitor Applications at Scale
In this article, the author discusses how to collect metrics and achieve anomaly detection from streaming data using Prometheus, Apache Kafka and Apache Cassandra technologies.
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Real-Time Data Processing Using Redis Streams and Apache Spark Structured Streaming
Structured Streaming, introduced with Apache Spark 2.0, delivers a SQL-like interface for streaming data. Redis Streams enables Redis to consume, hold and distribute streaming data between multiple producers and consumers. In this article, author Roshan Kumar walks us through how to process streaming data in real time using Redis and Apache Spark Streaming technologies.
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Back to the Future with Relational NoSQL
This article outlines some of the consistency issues NoSQL databases have with distributed transactions, showing how FaunaDB has solved the problems using the Calvin protocol and a virtual clock.
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Spark Application Performance Monitoring Using Uber JVM Profiler, InfluxDB and Grafana
In this article, author Amit Baghel discusses how to monitor the performance of Apache Spark based applications using technologies like Uber JVM Profiler, InfluxDB database and Grafana data visualization tool.
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Challenges of Building a Reliable Realtime Chat Service
Realtime chat has become a common feature of modern applications. These days not only communicators and social networks allow users to talk with each other over the Internet—chat is crucial in healthcare, e-commerce, gaming and many other industries.
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14 Things I Wish I’d Known When Starting with MongoDB
I’ve been a database person for an embarrassing length of time, but I only started working with MongoDB recently. When I was starting out with MongoDB, there are a few things that I wish I’d known about. With general experience, there will always be preconceptions of what databases are and what they do. In hopes of making it easier for other people, here is a list of common mistakes.
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A Critique of Resizable Hash Tables: Riak Core & Random Slicing
This fall, Wallaroo Labs will be releasing a large new feature set to our distributed data stream processing framework, Wallaroo. One of the new features requires a size-adjustable, distributed data structure to support growing & shrinking of compute clusters. It might be a good idea to use a distributed hash table to support the new feature, but what distributed hash algorithm should we choose?
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Picking an Active-Active Geo Distribution Strategy: Comparing Merge Replication and CRDT
Modern distributed applications are fuelling the growing demand for distributed active-active, multi-master databases. While most popular databases support multi-master deployment, different databases employ different techniques. LWW, MVCC, merge replication and CRDTs deliver eventual consistency, offering read and write access with local latency and remaining available during network partitions.