This article was originally posted on KDnuggets
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As observed in the Fortune article Survey shows huge popularity spike for Apache Spark:
“Apache Spark is the Taylor Swift of big data software. The open source technology has been around and popular for a few years. But 2015 was the year Spark went from an ascendant technology to a bona fide superstar.”
One of the reasons why Apache Spark has become so popular is because Spark provides data engineers and data scientists with a powerful, unified engine that is both fast (100x faster than Apache Hadoop for large-scale data processing) and easy to use. This allows data practitioners to solve their machine learning, graph computation, streaming, and real-time interactive query processing problems interactively and at much greater scale.
In this blog post, we will discuss some of the key terms one encounters when working with Apache Spark.
1. Apache Spark
Apache Spark is a powerful open-source processing engine built around speed, ease of use, and sophisticated analytics, with APIs in Java, Scala, Python, R, and SQL. Spark runs programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk. It can be used to build data applications as a library, or to perform ad-hoc data analysis interactively. Spark powers a stack of libraries including SQL, DataFrames, and Datasets, MLlib for machine learning, GraphX for graph processing, and Spark Streaming. You can combine these libraries seamlessly in the same application. As well, Spark runs on a laptop, Hadoop, Apache Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Apache Cassandra, Apache HBase, and S3.
It was originally developed at UC Berkeley in 2009. (Note that Spark’s creator Matei Zaharia has since become CTO at Databricks and faculty member at MIT.) Since its release, Spark has seen rapid adoption by enterprises across a wide range of industries. Internet powerhouses such as Netflix, Yahoo, and Tencent have eagerly deployed Spark at massive scale, collectively processing multiple petabytes of data on clusters of over 8,000 nodes. It has quickly become the largest open source community in big data, with over 1000 code contributors and with over 187,000 members in 420 Apache Spark Meetups groups.
At the core of Apache Spark is the notion of data abstraction as distributed collection of objects. This data abstraction, called Resilient Distributed Dataset (RDD), allows you to write programs that transform these distributed datasets.
RDDs are immutable distributed collection of elements of your data that can be stored in memory or disk across a cluster of machines. The data is partitioned across machines in your cluster that can be operated in parallel with a low-level API that offers transformations and actions. RDDs are fault tolerant as they track data lineage information to rebuild lost data automatically on failure.
Below is an Apache Spark code snippet using Python and RDDs to perform a word count.
Open textFile for Spark Context RDD
text_file = spark.textFile("hdfs://…")
Execute word count
text_file.flatMap(lambda line: line.split())
.map(lambda word: (word, 1))
.reduceByKey(lambda a, b: a+b)
Like an RDD, a DataFrame is an immutable distributed collection of data. Unlike an RDD, data is organized into named columns, like a table in a relational database. Designed to make large data sets processing even easier, DataFrame allows developers to impose a structure onto a distributed collection of data, allowing higher-level abstraction; it provides a domain specific language API to manipulate your distributed data; and makes Spark accessible to a wider audience, beyond specialized data engineers.
Below is an Apache Spark code snippet using SQL and DataFrames to query and join different data sources.
Read JSON file and register temp view
Execute SQL query
results = context.sql("""SELECT * FROM people JOIN json …""")
Introduced in Spark 1.6, the goal of Spark Datasets is to provide an API that allows users to easily express transformations on domain objects, while also providing the performance and benefits of the robust Spark SQL execution engine.
Note, starting in Spark 2.0, the DataFrame APIs will merge with Datasets APIs, unifying data processing capabilities across all libraries. Because of unification, developers now have fewer concepts to learn or remember, and work with a single high-level and type-safe API called Dataset. Conceptually, the Spark DataFrame is an alias for a collection of generic objects
Dataset[Row], where a Row is a generic untyped JVM object. Dataset, by contrast, is a collection of strongly-typed JVM objects, dictated by a case class you define, in Scala or Java.
// Define a case class that represents our type-specific Scala JVM Object
case class Person (email: String, iq: Long, name: String)
// Read JSON file and convert to Dataset using the case class
val ds = spark.read.json("…").as[Person]
Apache Spark provides a general machine learning library — MLlib — that is designed for simplicity, scalability, and easy integration with other tools. With the scalability, language compatibility, and speed of Spark, data scientists can solve and iterate through their data problems faster.
From the inception of the Apache Spark project, MLlib was considered foundational for Spark’s success. The key benefit of MLlib is that it allows data scientists to focus on their data problems and models instead of solving the complexities surrounding distributed data (such as infrastructure, configurations, and so on). The data engineers can focus on distributed systems engineering using Spark’s easy-to-use APIs, while the data scientists can leverage the scale and speed of Spark core. Just as important, Spark MLlib is a general-purpose library, providing algorithms for most use cases while at the same time allowing the community to build upon and extend it for specialized use cases. To review the key terms of machine learning, please refer to Matthew Mayo’s Machine Learning Key Terms, Explained.
6. ML Pipelines
Typically when running machine learning algorithms, it involves a sequence of tasks including pre-processing, feature extraction, model fitting, and validation stages. For example, when classifying text documents might involve text segmentation and cleaning, extracting features, and training a classification model with cross-validation. Though there are many libraries we can use for each stage, connecting the dots is not as easy as it may look, especially with large-scale datasets. Most ML libraries are not designed for distributed computation or they do not provide native support for pipeline creation and tuning.
The ML Pipelines is a High-Level API for MLlib that lives under the “spark.ml” package. A pipeline consists of a sequence of stages. There are two basic types of pipeline stages: Transformer and Estimator. A Transformer takes a dataset as input and produces an augmented dataset as output. E.g., a tokenizer is a Transformer that transforms a dataset with text into an dataset with tokenized words. An Estimator must be first fit on the input dataset to produce a model, which is a Transformer that transforms the input dataset. E.g., logistic regression is an Estimator that trains on a dataset with labels and features and produces a logistic regression model.
GraphX is the component in Apache Spark for graphs and graph-parallel computation. At a high level, GraphX extends the Spark RDD via a Graph abstraction: a directed multigraph with properties attached to each vertex and edge. To support graph computation, GraphX exposes a set of fundamental operators (e.g., subgraph, joinVertices, and aggregateMessages) as well as an optimized variant of the Pregel API. In addition, GraphX includes a growing collection of graph algorithms and builders to simplify graph analytics tasks.
8. Spark Streaming
Spark Streaming is an extension of the core Spark API that allows data engineers and data scientists to process real-time data from various sources including (but not limited to) Kafka, Flume, and Amazon Kinesis. This processed data can be pushed out to filesystems, databases, and live dashboards. Its key abstraction is a Discretized Stream or, in short, a DStream, which represents a stream of data divided into small batches. DStreams are built on RDDs, Spark’s core data abstraction. This allows Spark Streaming to seamlessly integrate with any other Spark components like MLlib and Spark SQL.
This unification of disparate data processing capabilities is the key reason behind Spark Streaming’s rapid adoption. It makes it very easy for developers to use a single framework to satisfy all their processing needs.
9. Structured Streaming
Introduced as part of Apache Spark 2.0, structured streaming is a high-level streaming built on top of the Spark SQL engine. It is a declarative API that extends DataFrames and Datasets to support batch, interactive, and streaming queries. The advantage of this approach is that it allows programmers to apply their experience working with static data sets (i.e. batch) and easily apply this to infinite data sets (i.e. streaming).
spark-packages.org is a community package index to track the growing number of open source packages and libraries that work with Apache Spark. Spark Packages makes it easy for users to find, discuss, rate, and install packages for any version of Spark and makes it easy for developers to contribute packages.
Spark Packages features integrations with various data sources, management tools, higher level domain-specific libraries, machine learning algorithms, code samples, and other Spark content. Examples packages include Spark-CSV (which is now included in Spark 2.0) and Spark ML integration packages including GraphFrames and TensorFrames.
11. Catalyst Optimizer
Spark SQL is one of the most technically involved components of Apache Spark. It powers both SQL queries and the DataFrame API. At the core of Spark SQL is the Catalyst optimizer, which leverages advanced programming language features (e.g. Scala’s pattern matching and quasiquotes) in a novel way to build an extensible query optimizer.
Catalyst is based on functional programming constructs in Scala and designed with these key two purposes:
- Easily add new optimization techniques and features to Spark SQL
- Enable external developers to extend the optimizer (e.g. adding data source specific rules, support for new data types, etc.)
As well, Catalyst supports both rule-based and cost-based optimization.
For more information, please refer to Deep Dive into Spark SQL’s Catalyst Optimizer and the webinar Apache Spark DataFrames: Simple and Fast Analysis of Structured Data.
Tungsten is the codename for the umbrella project to make changes to Apache Spark’s execution engine that focuses on substantially improving the efficiency of memory and CPU for Spark applications, to push performance closer to the limits of modern hardware. This effort includes the following initiatives:
- Memory Management and Binary Processing: leveraging application semantics to manage memory explicitly and eliminate the overhead of JVM object model and garbage collection
- Cache-aware computation: algorithms and data structures to exploit memory hierarchy
- Code generation: using code generation to exploit modern compilers and CPUs
- No virtual function dispatches: this reduces multiple CPU calls which can have a profound impact on performance when dispatching billions of times.
- Intermediate data in memory vs CPU registers: Tungsten Phase 2 places intermediate data into CPU registers. This is an order of magnitudes reduction in the number of cycles to obtain data from the CPU registers instead of from memory
- Loop unrolling and SIMD: Optimize Apache Spark’s execution engine to take advantage of modern compilers and CPUs’ ability to efficiently compile and execute simple for loops (as opposed to complex function call graphs).
For more information, please reference Project Tungsten: Bringing Apache Spark Closer to Bare Metal, Deep Dive into Spark SQL’s Catalyst Optimizer, and Apache Spark as a Compiler: Joining a Billion Rows per Second on a Laptop.
13. Continuous Applications
In Apache Spark 2.0, adding structure to Spark, through use of high-level DataFrames and Datasets APIs, accommodates a novel approach to look at real-time streaming. That is, look at streaming not as streaming but as either a static table of data (where you know all the data) or a continuous table of data (where new data is continuously arriving).
As such you can build end-to-end continuous applications, in which you can issue the same queries to batch as to real-time data, perform ETL, generate reports, update or track specific data in the stream. This combined batch & real-time query-capabilities to a structured stream is a unique offering—not many streaming engines offer it yet.