Category: Misc Languages

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

Anmol Sarna explains the pros and cons of using Apache Cassandra:

But as we know nothing is perfect. So is the Cassandra Database. What I mean by this is that you cannot have a perfect package. If you wish for one brilliant feature then you might have to compromise on the other features. In today’s blog, we will be going through some of the benefits of selecting Cassandra as your database as well as the problems/drawbacks that one might face if he/she chooses Cassandra for his/her application.
I have also written some blogs earlier which you can go through for reference if you want to know What Cassandra is, How to set it up and how it performs its Reads and Writes.

The only question we have is that should we or should we not pick Cassandra over the other databases that are available. So let’s start by having a quick look at when to use the Cassandra Database. This will give a clear picture to all those who are confused in decided whether to give Cassandra a try or not.

This is a level-headed analysis of Cassandra, so check it out.

Ayush Hooda explains a couple core principles behind functional programming:

A pure function can be defined like this:

• The output of a pure function depends only on(a) its input parameters and(b) its internal algorithm,which is unlike an OOP method, which can depend on other fields in the same class as the method.

• A pure function has no side effects, i.e., that it does not read anything from the outside world or write anything to the outside world. – For example, It does not read from a file, web service, UI, or database, and does not write anything either.

• As a result of those first two statements, if a pure function is called with an input parameter x an infinite number of times, it will always return the same result y. – For instance, any time a “string length” function is called with the string “Ayush”, the result will always be 5.

If I got to add one more thing, it’d be the idea that functions are first-class data types.  In other words, a function can be an input to another function, the same as any other data type like int, string, etc.  It takes some time to get used to that concept, but once you do, these types of languages become quite powerful.