And that is pretty wonderful.īesides that, Cassandra provides many of the same guarantees as other Hadoop-y databases - it works on a cluster and can span multiple data centers it can talk to Hadoop and Spark in such a way as to maintain data-locality mechanisms (although the importance of this aspect is questionable) the format of the data it stores is malleable to an extent it provides robust storage. No single node is special so no single node becomes a bottleneck for overall cluster performance, and there is no single point of failure. One of Cassandra's coolest features is the fact that it scales in a predictable way - every single node on a Cassandra cluster has the same role and works in the same way, there is no central gate keeper type node through which all traffic must pass or anything like that. Like other Big Data databases, it allows for malleable data structures. CassandraĬassandra is a distributed database based on Google BigTable and Amazon's Dynamo. On top of all this, Spark is the Apache Foundations top project. Spark also works with any Hadoop compatible storage, that makes converting from Hadoop to Spark isn't quite as hideous as it could be. Spark also does stuff that doesn't fall into the map-reduce way of thinking - for example, it allows for iterative processing, something vanilla Hadoop is ill-suited for. Spark aims to make better use of system resources, especially RAM, and is touted as being 10 times faster than Hadoop for some workloads.
Users can thus execute ad-hoc queries or submit larger jobs.
#Install spark ubuntu code
For one thing, it is easier to use, allowing users to specify map-reduce jobs by simply opening up a shell and writing code that is generally readable, maintainable and quick to write. Spark is a relatively recent addition to the Hadoop ecosystem and works to solve a few of the problems of vanilla Hadoop. Slower than it could be anyway - the technical details are beyond the scope of this tutorial though. Besides that, it only does map-reduce - there are many big data problems that simply cannot fit into that paradigm (or whatever you want to call it). A few scripting tools such as Apache Pig have been developed in order to abstract away from this, but it's still a problem. Writing map-reduce code can be tedious and has a lot of room for misguidedness - not everyone can do it. Firstly, it can be a bit horrible to use.
Hadoop was initially a tool (well, actually it was first a small yellow elephant, then it was a tool), and then the word started being used to refer to an ecosystem of compatible tools.
Hadoop solved a bunch of problems - it took one kind of algorithm and turned it into a distributed production line thus creating an efficient and robust system for solving very specific kinds of problems. Spark has been described as the swiss army knife of big data, but what does that mean? Spark started off as a replacement for Hadoop, and Hadoop is a sort of industry standard tool for doing large scale distributed map-reduce calculations. Below is a little description for each of the major ones we'll be dealing with here. This tutorial touches on quite a few different technologies.
#Install spark ubuntu software
The aim of this tutorial is to give you a starting point from which to configure your cluster for your specific application, and give you a few ways to make sure your software is running correctly. This tutorial, however, will deal with a single computer installation. Spark and Cassanrda exist for the sake of applications in Big Data, as such they are intended for installation on a cluster of computers possibly spread over multiple geographic locations.
#Install spark ubuntu how to
This tutorial is going to go through the steps required to install Cassandra and Spark on a Debian system and how to get them to play nice via Scala.
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