Category: Hadoop

Alex Woodie reports that Yahoo is open sourcing Pulsar:

Pulsar uses Apache Bookkeeper (committed by Yahoo to open source in 2011) as its durable storage mechanism. “With Bookkeeper, applications can create many independent logs, called ledgers,” Pulsar’s project page on GitHub says. “A ledger is an append-only data structure with a single writer that is assigned to multiple storage nodes (or bookies) and whose entries are replicated to multiple of these nodes.”

Pulsar uses brokers to serve topics. Each topic is assigned to a broker, and an individual broker can serve thousands of topics, Yahoo says. “The broker accepts messages from writers, commits them to a durable store, and dispatches them to readers,” Yahoo says.

My biases would lead me to still go with Kafka over Pulsar, but it’d be interesting to see a good comp between the two.

The Hue team shows off their new SQL editor’s autocomplete capabilities:

We’ve fine-tuned the live autocompletion for a better experience and we’ve introduced some options under the editor settings where you can turn off live autocompletion or disable the autocompleter altogether (if you’re adventurous). To access these settings open the editor and focus on the code area, press CTRL + , (or on Mac CMD + ,) and the settings will appear.

The autocompleter talks to the backend to get data for tables and databases etc. by default it will timeout after 5 seconds but once it has been fetched it’s cached for the next time around. The timeout can be adjusted in the Hue server configuration.

I haven’t used Hue in a while, but that’s a nice feature.  Just don’t use ANSI-89 syntax like in that first example…

I talk about Hadoop a good bit on Curated SQL.  Therefore, I think it’s worth mentioning the original MapReduce paper that Jeffrey Dean and Sanjay Ghemawat published in 2004:

MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model, as shown in the paper.

Programs written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data, scheduling the program’s execution across a set of machines, handling machine failures, and managing the required inter-machine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system.

Our implementation of MapReduce runs on a large cluster of commodity machines and is highly scalable: a typical MapReduce computation processes many terabytes of data on thousands of machines. Programmers find the system easy to use: hundreds of MapReduce programs have been implemented and upwards of one thousand MapReduce jobs are executed on Google’s clusters every day.

If you’ve never read this paper before, today might be a good day to do so.

Elton Stoneman has a new Pluralsight course out:

My latest Pluralsight course is out now:

Hadoop for .NET Developers

It takes you through running Hadoop on Windows and using .NET to write MapReduce queries – proving that you can do Big Data on the Microsoft stack.

The course has five modules, starting with the architecture of Hadoop and working through a proof-of-concept approach, evaluating different options for running Hadoop and integrating it with .NET.

I’ve liked Elton’s courses, as he’s one of the few trainers who really takes the time to show how you can integrate .NET languages into a Hadoop ecosystem; the general philosophy is “go learn Java and Scala and Python and …”