Learning Spark: Lightning-Fast Big Data Analysis
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The library is usable in Java, Scala, and Python as part of Spark applications, so that you can include it in complete workflows. GraphX is a graph computation engine built on top of Spark that enables users to interactively build, transform and reason about graph structured data at scale. It comes complete with a library of common algorithms.
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Spark Core is the underlying general execution engine for the Spark platform that all other functionality is built on top of. It provides in-memory computing capabilities to deliver speed, a generalized execution model to support a wide variety of applications, and Java, Scala, and Python APIs for ease of development.
Engineered from the bottom-up for performance, Spark can be x faster than Hadoop for large scale data processing by exploiting in memory computing and other optimizations. Spark is also fast when data is stored on disk, and currently holds the world record for large-scale on-disk sorting.
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This includes a collection of over operators for transforming data and familiar data frame APIs for manipulating semi-structured data. A Unified Engine Spark comes packaged with higher-level libraries, including support for SQL queries, streaming data, machine learning and graph processing. These standard libraries increase developer productivity and can be seamlessly combined to create complex workflows. Try Apache Spark on the Databricks cloud for free The Databricks Unified Analytics Platform offers 5x performance over open source Spark, collaborative notebooks, integrated workflows, and enterprise security — all in a fully managed cloud platform.
The open source Apache Spark project can be downloaded here. Databricks Inc.
Apache Spark Ecosystem. Spark also makes it possible to write code more quickly as you have over 80 high-level operators at your disposal. Written in Java for MapReduce it has around 50 lines of code, whereas in Spark and Scala you can do it as simply as this:.
Learning Spark: Lightning-Fast Big Data Analysis / Edition 1
Another important aspect when learning how to use Apache Spark is the interactive shell REPL which it provides out-of-the box. Using REPL, one can test the outcome of each line of code without first needing to code and execute the entire job. The path to working code is thus much shorter and ad-hoc data analysis is made possible.
The Spark core is complemented by a set of powerful, higher-level libraries which can be seamlessly used in the same application. Additional Spark libraries and extensions are currently under development as well. Spark Core is the base engine for large-scale parallel and distributed data processing. It is responsible for:. Spark introduces the concept of an RDD Resilient Distributed Dataset , an immutable fault-tolerant, distributed collection of objects that can be operated on in parallel. An RDD can contain any type of object and is created by loading an external dataset or distributing a collection from the driver program.
The transformations are only actually computed when an action is called and the result is returned to the driver program. This design enables Spark to run more efficiently. For example, if a big file was transformed in various ways and passed to first action, Spark would only process and return the result for the first line, rather than do the work for the entire file.
Learning Spark: Lightning-Fast Big Data Analysis | numahub
By default, each transformed RDD may be recomputed each time you run an action on it. However, you may also persist an RDD in memory using the persist or cache method, in which case Spark will keep the elements around on the cluster for much faster access the next time you query it. It originated as the Apache Hive port to run on top of Spark in place of MapReduce and is now integrated with the Spark stack. In addition to providing support for various data sources, it makes it possible to weave SQL queries with code transformations which results in a very powerful tool.
Learning Spark: Lightning-Fast Big Data Analysis
Below is an example of a Hive compatible query:. Spark Streaming supports real time processing of streaming data, such as production web server log files e. Under the hood, Spark Streaming receives the input data streams and divides the data into batches. Next, they get processed by the Spark engine and generate final stream of results in batches, as depicted below.
The Spark Streaming API closely matches that of the Spark Core, making it easy for programmers to work in the worlds of both batch and streaming data. Some of these algorithms also work with streaming data, such as linear regression using ordinary least squares or k-means clustering and more on the way. GraphX is a library for manipulating graphs and performing graph-parallel operations. It provides a uniform tool for ETL, exploratory analysis and iterative graph computations.
Apart from built-in operations for graph manipulation, it provides a library of common graph algorithms such as PageRank. I came across an article recently about an experiment to detect an earthquake by analyzing a Twitter stream.
webmail.cmnv.org/hiqip-hangouts-spy-program.php Interestingly, it was shown that this technique was likely to inform you of an earthquake in Japan quicker than the Japan Meteorological Agency. Even though they used different technology in their article, I think it is a great example to see how we could put Spark to use with simplified code snippets and without the glue code. We could easily use Spark Streaming for that purpose as follows:.
Then, we would have to run some semantic analysis on the tweets to determine if they appear to be referencing a current earthquake occurrence. The authors of the paper used a support vector machine SVM for this purpose. A resulting code example from MLlib would look like the following:.
If we are happy with the prediction rate of the model, we could move onto the next stage and react whenever we discover an earthquake. To detect one we need a certain number i. Note that, for tweets with Twitter location services enabled, we would also extract the location of the earthquake.
Armed with this knowledge, we could use SparkSQL and query an existing Hive table storing users interested in receiving earthquake notifications to retrieve their email addresses and send them a personalized warning email, as follows:. In the game industry, processing and discovering patterns from the potential firehose of real-time in-game events and being able to respond to them immediately is a capability that could yield a lucrative business, for purposes such as player retention, targeted advertising, auto-adjustment of complexity level, and so on.
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In the e-commerce industry, real-time transaction information could be passed to a streaming clustering algorithm like k-means or collaborative filtering like ALS. Results could then even be combined with other unstructured data sources, such as customer comments or product reviews, and used to constantly improve and adapt recommendations over time with new trends. In the finance or security industry, the Spark stack could be applied to a fraud or intrusion detection system or risk-based authentication. To sum up, Spark helps to simplify the challenging and computationally intensive task of processing high volumes of real-time or archived data, both structured and unstructured, seamlessly integrating relevant complex capabilities such as machine learning and graph algorithms.