What Is Apache Spark?
Apache Spark is an open-source framework for processing huge volumes of data (big data) with speed and simplicity. It is suitable for applications based on big data. Spark can be used with a Hadoop environment, standalone or in the cloud. It can run in Hadoop clusters through YARN or Spark’s standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive and any Hadoop Input Format. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning
It was developed at the University of California and then later offered to the Apache Software Foundation. Thus, it belongs to the open-source community and can be very cost-effective, which further allows amateur developers to work with ease.
The main purpose of Spark is that it offers developers with an application framework that works around a centered data structure. Spark is also extremely powerful and has the innate ability to quickly process massive amounts of data in a short span of time, thus offering extremely good performance. This makes it a lot faster than what is said to be its closest competitor, Hadoop.
Why Spark Is So Important Over Hadoop
Apache Spark has always been known to trump Hadoop in several features, which probably explains why it remains so important. One of the prime reasons for this would be to consider its processing speed. In fact, as stated above already, Spark offers about 100 times faster processing than Hadoop’s MapReduce for the same amount of data. It also uses significantly fewer resources as compared to Hadoop, thereby making it cost-effective.
Another key aspect where Spark has the upper hand is in terms of compatibility with a resource manager. Apache Spark is known to run with Hadoop, just as MapReduce does. As for Apache Spark, however, it can work with other resource managers such as YARN or Mesos. Data scientists often cite this as one of the biggest areas where Spark really outdoes Hadoop.
When it comes to ease of use, Spark again happens to be a lot better than Hadoop. Spark has APIs for several languages such as Scala Java and , besides having the likes of Spark SQL. It is relatively simple to write user-defined functions. It also happens to boast an interactive mode for running commands. Hadoop, on the other hand, is written in Java and has earned the reputation of being pretty difficult to program, although it does have tools that assist in the process.
What Are Spark’s Unique Features?
Apache Spark has some unique features that truly distinguish it from many of its competitors in the business of data processing. Some of these have been outlined briefly below.
One of the unique aspects of Apache Spark is its unique “in-memory” technology that allows it to be an extremely good data processing system. In this technology, Spark loads all of the data to the internal memory of the system and then unloads it on the disk later. This way, a user can save a part of the processed data on the internal memory and leave the remaining on the disk.Spark also has an innate ability to load necessary information to its core with the help of its machine learning algorithms. This allows it to be extremely fast.
Spark’s core manages several important functions like setting tasks and interactions as well as producing input/output operations. It can be said to be an RDD, or resilient distributed dataset. Basically, this happens to be a mix of data that is spread across several machines connected via a network. The transformation of this data is created by a four-step method, comprised of mapping the data, sorting it, reducing it and then finally, joining the data.
Following this step is the release of the RDD, which is done with support from an API. This API is a union of three languages: Scala, Java and Python.
Apache Spark’s SQL has a relatively new data management solution called SchemaRDD. This allows the arrangement of data into many levels and can also query data via a specific language.
Apache Spark comes with the ability to process graphs or even information that is graphical in nature, thus enabling the easy analysis with a lot of precision.
This is a prime part of Spark that allows it to stream large chunks of data with help from the core. It does so by breaking the large data into smaller packets and then transforming them, thereby accelerating the creation of the RDD.
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk. Apache Spark has an advanced DAG execution engine that supports acyclic data flow and in-memory computing.
Ease of Use
Write applications quickly in Java, Scala, Python, R.
Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python and R shells.
Word count in Spark’s Python API:
text_file = spark.textFile(“hdfs://…”)
text_file.flatMap(lambda line: line.split())
.map(lambda word: (word, 1))
.reduceByKey(lambda a, b: a+b)
Combine SQL, streaming, and complex analytics.
Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming.
You can combine these libraries seamlessly in the same application.
Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, and S3.
You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, or on Apache Mesos. Access data in HDFS, Cassandra, HBase, Hive, Tachyon, and any Hadoop data source.