Tag Archives: Spark

Flink vs. Spark

Recently, the Flink project has been in the news as a relatively new big data stream processing framework in the Apache stack.  Since our team at Intuit IDEA is implementing Spark Streaming for big data streaming I was curious how Flink compares to Spark Streaming.

Similarities: Both Spark and Flink support big data processing in Scala, Java and Python.  For instance, here is the word count in Flink Scala:

For comparison, here is the word count problem in Spark Scala:

FlinkSimilarly, both Flink and Spark Streaming support processing in either of the two principal modes of big data processing, batch and streaming mode.  Like Spark, Flink shines with in-memory processing and query optimization.  Also, both frameworks also provide for graceful degradation from in-memory to out-of-core algorithms for very large distributed datasets.  Spark and Flink support machine learning, via Spark ML and FlinkML, respectively.  Lastly, both Spark and Flink support graph processing, via Spark GraphX and Flink Gelly, respectively.

spark-logoDifferences: While Spark Streaming processes data streams as “micro-batches“, which are windows as small as 500 milliseconds, Flink has a true streaming API that can process individual data records. However, Flink does not yet support SQL access, as Spark SQL does, which is a feature Flink plans to add soon. Also, at the time of this writing, in July 2015, Spark appears to have considerably more industry commitment, via companies such as Databricks and IBM, which should be important for community involvement and support of production implementations.

Coming Soon: one of Flink’s upcoming announcements is the graduation of Flink’s streaming from “beta” status. Other enhancements will expand the FlinkML Machine Learning library, as well as the Gelly graph processing library and introduce new algorithms to both libraries.

Volker Markl, one of Flink’s creators, shares more details in his interview with Roberto V. Zicari of ODBMS Industry Watch.


Spark on Cloudera

Apache Spark is a platform for processing big data through streaming.  Streaming can be much faster than disk-based processing offered by traditional Hadoop installations.  Here’s what Cloudera has to say about Spark.

Use cases: Apache Spark supports batch, streaming, and interactive analytics on all your data, enabling historical reporting, interactive analysis, data mining, real-time insights.

Support: Cloudera offers commercial support for Spark with Cloudera Enterprise.

Performance: Spark is 10-100x faster than MapReduce analysts for iterative algorithms that are often used by analysts and data scientists.  Performance benefits materialize both in memory and on disk.

Language support: Spark supports Java, Scala, and Python.  It is not necessary to write “map” and “reduce” operators.

Integration: Spark is integrated with CDH and can read any data in HDFS and deployed through Cloudera Manager.

Features: API for working with streams, exactly-once semantics, fault tolerance, common code for batch and streaming, joining streaming data to historical data.

Differences vs. Storm: Spark Streaming can recover lost work and deliver exactly-once semantics out of the box.

For more details, see Cloudera’s discussion of Spark.

Cloudera Summarizes 2014

Screen Shot 2014-12-23 at 8.04.48 AM

In his letter from Cloudera, CEO Tom Reilly made a few interesting points.

$900 million round of funding

Cloudera secured a $900 million round of funding earlier this year, one of the largest ever in enterprise software. The majority of the investment came from Intel. Tom Reilly calls out security encryption at the chip level as an outcome of the Intel relationship.  Cloudera now has over 800 team members.

Acquisition of Gazzang and DataPad

Gazzang reportedly enables the industry’s first and only fully secure and regulation compliant Hadoop platform. DataPad has created a Python-based framework that simplifies data processing and analysis with Cloudera Enterprise.


The Cloudera partnerships listed are with Microsoft Azure, MongoDB, EMC, and Teradata.

Hadoop and Enterprise Data Warehousing (EDW)

Cloudera has made two webinars with Ralph Kimball on EDW available: Hadoop 101 for EDW Professionals
and EDW 101 for Hadoop Professionals.Screen Shot 2014-12-23 at 8.06.51 AM

Apache Spark

Apache Spark made huge strides, says Tom Reilly, and is well on its way to becoming the successor to MapReduce.


Cloudera believes Impala has continued to be the Hadoop SQL engine of choice.