Spark’s distributed data-sharing concept is called “Resilient Distributed Datasets,” or RDD. RDDs are fault-tolerant collections of objects partitioned across a cluster that can be queried in parallel and used in a variety of workload types. RDDs are created by applying operations called “transformations” with map, filter, and groupBy clauses. They can persist in memory for rapid reuse. If an RDD data does not fit in memory, Spark will overflow it to disk.
If you’re not familiar with Spark, now’s as good a time as any to learn.
When working with large datasets, you will have bad input that is malformed or not as you would expect it. I recommend being proactive about deciding for your use case, whether you can drop any bad input, or you want to try fixing and recovering, or otherwise investigating why your input data is bad.
A filter command is a great way to get only your good input points or your bad input data (If you want to look into that more and debug). If you want to fix your input data or to drop it if you cannot, then using a
flatMap()operation is a great way to accomplish that.
This is a good set of tips.
The recently released sparklyr package by RStudio has made processing big data in R a lot easier. sparklyr is an R interface to Spark that allows users to use Spark as the backend for dplyr, one of the most popular data manipulation packages. sparklyr provides interfaces to Spark packages and also allows users to query data in Spark using SQL and develop extensions for the full Spark API.
You can also install sparklyr locally and point to a Spark cluster.
Spark provides a comprehensive framework to manage big data processing with a variety of data set types including text and graph data. It can also handle batch pipelines and real-time streaming data. Using Spark libraries, you can create big data analytics apps in Java, Scala, Clojure, and popular R and Python languages.
Spark brings analytics pros an improved MapReduce type query capability with more performant data processing in memory or on disk. It can be used with datasets that are larger than the aggregate memory in a cluster. Spark also has savvy lazy evaluation of big data queries which helps with workflow optimization and reuse of intermediate results in memory. TheSpark API is easy to learn.
One of my taglines is, Spark is not the future of Hadoop; Spark is the present of Hadoop. If you want to get into this space, learn how to work with Spark.
Next, we’ll define a DataFrame by loading data from a CSV file, which is stored in HDFS.
facebook_combined.txtcontains two columns to represent links between network nodes. The first column is called source (
src), and the second is the destination (
dst) of the link. (Some other systems, such as Gephi, use “source” and “target” instead.)
First we define a custom schema, and than we load the DataFrame, using
It sounds like Spark graph database engines are early in their lifecycle, but they might already be useful for simple analysis.
Overview of Spark Streaming.
Fault-tolerance Semantics & Performance Tuning.
Spark Streaming Integration with Kafka.
Click through for the slide deck. Combine that with the AWS blog post on the same topic and you get a pretty good intro.
Over the past couple of years we’ve heard time and time again that people want a native dplyr interface to Spark, so we built one! sparklyr also provides interfaces to Spark’s distributed machine learning algorithms and much more. Highlights include:
Interactively manipulate Spark data using both dplyr and SQL (via DBI).
Filter and aggregate Spark datasets then bring them into R for analysis and visualization.
Create extensions that call the full Spark API and provide interfaces to Spark packages.
Integrated support for establishing Spark connections and browsing Spark DataFrames within the RStudio IDE.
So what’s the difference between sparklyr and SparkR?
@zedoring sparkR is “inspired by dplyr” and distributed with Spark, sparklyr is a proper dplyr back-end which will be on CRAN.
— Jeff Allen (@TrestleJeff) June 28, 2016
This might be the package I’ve been awaiting.
Stream processing walkthrough
The entire pattern can be implemented in a few simple steps:
Set up Kafka on AWS.
Spin up an EMR 5.0 cluster with Hadoop, Hive, and Spark.
Create a Kafka topic.
Run the Spark Streaming app to process clickstream events.
Use the Kafka producer app to publish clickstream events into Kafka topic.
Explore clickstream events data with SparkSQL.
This is a pretty easy-to-follow walkthrough with some good tips at the end.
In the previous blog, we looked at on converting the CSV format into Parquet format using Hive. It was a matter of creating a regular table, map it to the CSV data and finally move the data from the regular table to the Parquet table using the Insert Overwrite syntax. In this blog we will look at how to do the same thing with Spark using the dataframes feature.
Most of the code is basic setup; writing to Parquet is really a one-liner.
Hello geeks, we have discussed how to start programming with Spark in Scala. In this blog, we will discuss how we can use Hive with Spark 2.0.
When you start to work with Hive, you need HiveContext (inherits SqlContext), core-site.xml,hdfs-site.xml, and hive-site.xml for Spark. In case you don’t configure hive-site.xml then the context automatically creates metastore_db in the current directory and creates warehousedirectory indicated by HiveConf(which defaults user/hive/warehouse).
Rahul has made his demo code available on GitHub.