Notes about Cutting-Edge Technologies and Everything

Joins are prevalent operations in many data processing applications. Most data processing systems feature APIs that make joining data sets very easy. However, the internal algorithms for join processing are much more involved – especially if large data sets need to be efficiently handled. Therefore, join processing serves as a good example to discuss the salient design points and implementation details of a data processing system. In this blog post, we cut through Apache Flink’s layered architecture and take a look at its internals with a focus on how it handles joins. Specifically, I will: show how easy it is to join data sets using Flink’s fluent APIs, discuss basic distributed join strategies, Flink’s join implementations, and its memory management, talk about Flink’s optimizer that automatically chooses join strategies, show some performance numbers for joining data sets of different sizes, and finally briefly discuss joining of co-located and pre-sorted data sets. Details:...

Analysts and data scientists⎯not to mention business executives⎯want Big Data not for the sake of the data itself, but for the ability to work with and learn from that data. As other users become more savvy, they also want more access. But too many inefficient queries can create a bottleneck in the system. The good news is that Apache™ Hive 0.14—the standard SQL interface for processing, accessing and analyzing Apache Hadoop® data sets—is now powered by Apache Calcite. Calcite is an open source, enterprise-grade Cost-Based Logical Optimizer (CBO) and query execution framework. The main goal of a CBO is to generate efficient execution plans by examining the tables and conditions specified in the query, ultimately cutting down on query execution time and reducing resource utilization. Calcite has an efficient plan pruner that can select the cheapest query plan. All SQL queries are converted by Hive to a physical operator tree, optimized and converted to Tez/MapReduce jobs, then executed ...

In some recent blog posts, we’ve explained why we believe it’s crucial to adopt a four‑tier application architecture in which applications are developed and deployed as sets of microservices . It’s becoming increasingly clear that if you keep using development processes and application architectures that worked just fine ten years ago, you simply can’t move fast enough to capture and hold the interest of mobile users who can choose from an ever‑growing number of apps. Switching to a microservices architecture creates exciting opportunities in the marketplace for companies. For system architects and developers, it promises an unprecedented level of control and speed as they deliver innovative new web experiences to customers. But at such a breathless pace, it can feel like there’s not a lot of room for error. In the real world, you can’t stop developing and deploying your apps as you retool the processes for doing so. You know that your future success depends on transitio...

Hadoop is on its way to becoming the de facto platform for the next-generation of data-based applications, but it’s not without flaws. Ironically, one of Hadoop’s biggest shortcomings now is also one of its biggest strengths going forward — the Hadoop Distributed File System. Within the Apache Software Foundation, HDFS is always improving in terms of performance and availability. Honestly, it’s probably fine for the majority of Hadoop workloads that are running in pilot projects, skunkworks projects or generally non-demanding environments. And technologies such as HBase that are built atop HDFS speak to its versatility as storage system even for non-MapReduce applications. But if the growing number of options for replacing HDFS signifies anything, it’s that HDFS isn’t quite where it needs to be. Some Hadoop users have strict demands around performance, availability and enterprise-grade features, while others aren’t keen of its direct-attached storage (DAS) architecture. Concerns arou...

Apache HBase is all about giving you random, real-time, read/write access to your Big Data, but how do you efficiently get that data into HBase in the first place? Intuitively, a new user will try to do that via the client APIs or by using a MapReduce job with TableOutputFormat, but those approaches are problematic, as you will learn below. Instead, the HBase bulk loading feature is much easier to use and can insert the same amount of data more quickly. This blog post will introduce the basic concepts of the bulk loading feature, present two use cases, and propose two examples: https://blog.cloudera.com/blog/2013/09/how-to-use-hbase-bulk-loading-and-why/

Detailed presentation on Apache Flink: https://www.slideshare.net/KostasTzoumas/apache-flink-api-runtime-and-project-roadmap