Impala is the open source version of Dremel, Google’s proprietary big data query solution. A first beta is available and the production version is foreseen for Q1 2013.
However the real revolution will only get better when Doug Cutting [the creator of Lucene, Hadoop, etc.]’s Trevni is integrated into Impala. Trevni is a new columnar data storage format that promises superior performance for reading large columnar stored data sets.
Impala+Trevni is promising real-time big data queries with multiple joins that are on par in performance but have more functionality than Google’s Dremel…
The website defines Spark as a MapReduce-like cluster computing framework designed to support low-latency iterative jobs. However it would be easier to say that Spark is Hadoop for real-time.
Spark allows you to run MapReduce jobs together with your data on distributed machines. Unlike Hadoop Spark can distributed your data in slices and store it in memory hence your processing and data are co-located in memory. This gives an enormous performance boost. Spark is more than MapReduce however. It offers a new distributed framework on which different distributed computing paradigms can be modelled. Examples are: Hadoop’s Hive => Shark (40x faster than Hive), Google’s Pregel / Apache’s Giraph => Bagel, etc. An upcoming Spark Streaming is supposed to bring real-time streaming to the framework.
The excellent part
Spark is written in Scala and has a very straight forward syntax to run applications from the command line or via compiled code. The possibilities to run iterative operations over large datasets or very compute intensive operations in parallel, make it ideal for big data analytics and distributed machine learning.
The points for improvement
In order to use Spark, you need to install Mesos. Mesos is a framework for distributed computing that was also developed by Berkeley. So in a sense they are eating their own dog food. Unfortunately Mesos is not written in scala so installing Spark becomes a mix of make’s, ant’s, .sh, XML, properties, .conf, etc. It would not be bad if Mesos would have consistent documentation but due to incubation into Apache the installation process is currently undergoing changes and is not straightforward.
Spark allows to connect to Hadoop, Hbase, etc. However running Hadoop on top of Mesos is “experimental” to say the least. The integration with Hadoop should be lighter. At the end only access to HDFS, SequenceFiles, etc. is required. This should not mean that a complete Hadoop should be installed and Spark should be recompiled for each specific Hadoop version.
If Spark wants to become as successful as Hadoop, then they should learn from Hadoop’s mistakes. Complex installation is a big problem because Spark needs to be installed on many machines. The Spark team should take a look at Ruby’s Rubygems, Node.js’s npm, etc. and make the installation simple, ideally via Scala’s package manager, although it is less popular.
If possible the team should drop Mesos as a prerequisite and make it optional. One of Spark’s competitors is Storm & Trident, you can install a Storm cluster in minutes and have a one click command to run Storm on an EC2 cluster.
It would be nice if there would be an integration SDK that allows extensions to be plugged-in. Integrations with Cassandra, Redis, Memcache, etc. could be developed by others. Also looking at a distribution in which Cassandra’s Brisk is used to mimic Hive and HDFS (a.k.a. CassandraFS) and have it all pre-bundled with Shark, could be an option. Spark’s in-memory execution and read speed, combined with Cassandra’s write speed, should make for a pretty quick and scalable solution. Ideally without the need to fight with namenodes, datanodes, jobtrackers, etc. and other Hadoop hard-to-configure inventions…
The conclusion is that distributed computing and programming is already hard enough by itself. Programmers should be focusing on their algorithms and not need a professional admin to get them started.
All-in-all Spark, Shark, Streaming Spark, Bagel, etc. have a lot of potential, it is just a little bit rough around the edges…
Update: I am reviewing my opinion about Mesos. See the Mesos post.
With Hadoop/Hbase/Hive, Cassandra, etc. you can store and manipulate peta-bytes of data. But what if you want to get nice looking reports or compare data held in a NoSQL solution with data held elsewhere? There have been two market leaders in the Open Source business intelligence space that are putting all their firepower onto Big Data now.
Pentaho Big Data seems to be a bit further ahead. They offer a graphical ETL tool, a report designer and a business intelligence server. These are existing tools but support for Hadoop HDFS, Map-Reduce, Hbase, Hive, Pig, Cassandra, etc. have been added.
Jaspersoft’s Open Source Big Data strategy is a little bit behind because connectors are not included yet into the main product and several are still in beta quality and with missing documentation.
Both companies will accelerate the adoption of big data since the main problem with Big Data is easy reporting. Unstructured data is harder to format into a very structured report than structured data. Any solutions that will make this possible and additionally are Open Source are very welcome in times of cost cutting…