Introduction to Spark for Developers and Data Scientists

What is Spark?

Spark is “a fast and general engine for large-scale data processing”. – http://spark.apache.org/

Spark is also one of the most popular open source frameworks for big data, based on number of contributors. Let us find out why this is the case.

When do you use Spark?

Suppose you would like to analyze a data set: perform ETL or data munging, then run SQL queries such as grouping and aggregations against the data, and maybe apply a machine learning algorithm. When the data size is small, everything will run quickly on a single machine, and you can use analysis tools like Pandas (Python), R, or Excel, or write your own scripts. But, for larger data sets, data processing will be too slow on a single machine, and then you will want to move to a cluster of machines. This is when you would use Spark.

You could probably benefit from Spark if:

•       Your data is currently stored in Hadoop / HDFS.
•       Your data set contains more than 100 million rows.
•       Ad-hoc queries take longer than 5 minutes to complete.

What Spark is Not, typical architecture

Spark can be a central component to a big data system, but it is not the only component. It is not a distributed file system: you would typically store your data on HDFS or S3. Nor is Spark a NoSQL database: Cassandra or HBase would be a better place for horizontally scalable table storage. And, it is not a message queue: you would use Kafka or Flume to collect streaming event data. Spark is, however, a compute engine which can take input or send output to all of these other systems.

How do you use Spark?

Implemented in Scala, Spark can be programmed in Scala, Java, Python, SQL, and R. However, not all of the latest functionality is immediately available in all languages.

What kind of operations does Spark support?

Spark SQL. Spark supports batch operations involved in ETL and data munging, via the DataFrame API. It supports parsing different input formats, such as JSON or Parquet. Once the raw data is loaded, you can easily compute new columns from existing columns. You can slice and dice the data by filtering, grouping, aggregating, and joining with other tables. Spark supports relational queries, which you can express in SQL or through the DataFrame API.

Spark Streaming. Spark also provides scalable stream processing. Given an input data stream, for example, coming from Kafka, Spark allows you to perform operations on the streaming data, such as map, reduce, join, and window.

MLlib. Spark includes a machine learning library, with scalable algorithms for classification, regression, collaborative filtering, clustering, and more. If training your dataset on a single machine takes too long, you might consider cluster computing with Spark.

GraphX. Finally, GraphX is a component in Spark for scalable batch processing on graphs.

As you may have noticed by now, Spark processing is batch oriented. It works best when you want to perform the same operation on all of your data, or a large subset of your data. Even with Spark Streaming, you operate on small batches of the data stream, rather than one event at a time.

Spark vs. Hadoop MapReduce, Hive, Impala

How does Spark compare with other big data compute engines? Unlike Hadoop MapReduce, Spark caches data in memory for huge performance gains when you have ad-hoc queries or iterative workloads, which are common in machine learning algorithms. Hive and Impala both run SQL queries at scale; the advantage of Spark over these systems is (1) the convenience of writing both queries and UDFs in the same language, such as Scala, and (2) support for machine learning algorithms, streaming data, and graph processing within the same system.

Conclusion

This overview has explained what Spark is, when to use it, what kinds of operations it supports, and how it compares with other big data systems. To learn more, please take a look at the Spark website (http://spark.apache.org/).