Category: Misc Languages

Niels Berglund shows how you can use sp_execute_external_script to run Java code in SQL Server 2019:

In SQL Server 2019 Microsoft added the ability to execute custom Java code along the same lines we execute R and Python, and this blog post intends to give an introduction of how to install and enable the Java extension, as well as execute some very basic Java code. In future posts, I drill down how to pass data back and forth between SQL Server and Java.

There may very well be future posts discussing how the internals differ between Java and R/Python, but I want to talk about that a little bit in this post as well, as it has an impact on how we write and call Java code.

The not-so-secret here is that Java itself is less interesting of a language than, say, Scala.  And the reason you’d support Scala?  To interact with an Apache Spark cluster.  I think that’s a big part of why you’d want the installer to load Java 1.8 instead of 1.9 or later (which contain API changes which break Spark).  Definitely give this a careful read, as there are more working parts and more gotchas than R or Python support.

Adrian Colyer reviews a paper on a multi-system caching algorithm:

The thing about this common pattern is that we need to wait for all of these back-end requests to complete before returning to the user. So improving the average latency of these requests doesn’t help us one little bit.

Since each request must wait for all of its queries to complete, the overall request latency is defined to be the latency of the request’s slowest query. Even if almost all backends have low tail latencies, the tail latency of the maximum of several queries could be high.

(See ‘The Tail at Scale’).

The user can easily see P99 latency or greater.

Techniques to mitigate tail latencies include making redundant requests, clever use of scheduling, auto-scaling and capacity provisioning, and approximate computing. Robin Hood takes a different (complementary) approach: use the cache to improve tail latency!

Robin Hood doesn’t necessarily allocate caching resources to the most popular back-ends, instead, it allocates caching resources to the backends (currently) responsible for the highest tail latency.

This is a great review of an interesting algorithm.

Charmy Garg walks us through some of the basics of modeling tables in Cassandra:

Two basic goals in Cassandra which we should keep in mind:

• Spread data evenly around the cluster – You want every node in the cluster to have roughly the same amount of data. Rows are spread around the cluster based on a hash of the partition key, which is the first element of the PRIMARY KEY. So, the key to spreading data evenly is this: pick a good primary key.

• Minimize the number of partitions read – Partitions are groups of rows that share the same partition key. When you issue a read query, you want to read rows from as few partitions as possible. Why is this important? [Each partition may reside on a different node. The coordinator will generally need to issue separate commands to separate nodes for each partition you request. This adds a lot of overhead and increases the variation in latency. Furthermore, even on a single node, it’s more expensive to read from multiple partitions than from a single one due to the way rows are stored.]

Charmy also has a couple of pitfalls that people used to the relational database model may hit.

Bill Fellows shows us how to read from an Excel file on a machine without Microsoft Office installed:

A common problem working with Excel data is Excel itself. Working with it programatically requires an installation of Office, and the resulting license cost, and once everything is set, you’re still working with COM objects which present its own set of challenges. If only there was a better way.

Enter, the better way – EPPlus. This is an open source library that wraps the OpenXml library which allows you to simply reference a DLL. No more installation hassles, no more licensing (LGPL) expense, just a simple reference you can package with your solutions.

Let’s look at an example.

Read on for the example.  A couple alternatives I like are readxl and XLConnect in R.

Jyoti Sachdeva explains the concept of higher-order functions and shares an example in Scala:

In this blog, I’m going to explain higher-order functions.

A higher order function takes other function as a parameter or return a function as a result.

This is possible because functions are first-class value in scala. What does that mean?

It means that functions can be passed as arguments to other functions and functions can return other function.

The map function is a classic example of a higher order function.

Higher-order functions are one of the key components to functional programming and allows us to reason in small chunks at a time