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In the landscape of Big Data, Apache Spark and Flink are two of the major technologies. There is always some confusion attached about them as well about the features and capabilities of both these technologies. So, today in this post let us compare both apart from a brief introduction of these most used technologies.
Difference Between Apache Spark and Apache Flink
Apache Spark and Flink have captured the Hadoop Big Data market rapidly, and various job roles are available for these technologies. There were some limitations of Spark due to which Flunk came into the picture. Here we will discuss them in detail, so keep on reading to explore more about them.
Introduction of Apache Spark
Apache Spark is an open-source cluster computing framework, and the technology has a large user global base. The base languages used to write Spark are R, Java, Python, and Scala that gives an API to the programmers to build a fault-tolerant and read-only multi-set of data items. In a short time (May 204) since its release, it has to grasp large market share just due to its high speed, ability to handle sophisticated analytical requirements, and ease of use.
Apache Spark was introduced to minimize several limitations of Hadoop MapReduce and Big Data technologies. Its speed was much faster than MapReduce, and one of the factors that makes it more powerful is its ability to hold intermediate results in-memory itself. Here the data is not written back on disk or read from that again that may become difficult, especially for iteration-based use cases. So, here are some of the major advantages of Spark:
- Ease of Use: The APIs of Apache Spark is easy to use that is built for operating on large data sets
- High-Speed: Apache Spark can execute the process in batches, and so at a time it can run and process the jobs at 10 to 100 times faster than MapReduce. High-speed does not mean that the user will have to compromise with its disk data writing speed; instead, it is the world record holder in terms of large-scale on-disk sorting.
- In-memory data sharing: Different jobs may have to share data within memory, and it can make it an ideal choice for interactive, iterative, and event stream processing tasks.
- Unified Engine: Spark can run on Hadoop that is why it can also run on Hadoop cluster manager(YARN) and HDFS, HBase, etc. like underlying storage. However, users can also use Spark independently without Hadoop by joining it with other cluster managers and storage platforms like Amazon S3 and Cassandra. It also has many higher-level libraries as well that can support SQL queries, machine learning, graph processing, and data streaming.
- Choose From Scala, Java, and Python: You are not bound to use any single language when you use Spark, even you are open to using any of the popular languages like R, Python, Java and even Clojure.
- Expanding and active User Community: due to active user community Spark user can lead to a stable Spark release, just within two years of its release. Due to this, it has been accepted worldwide, and its popularity is rising continuously.
Introduction of Apache Flink
Apache Flink is the latest entrant to the open-source frameworks, used for Big Data Analytics, and trying to replace MapReduce one similar one is Spark. Flink was released in March 2016 and was introduced just for in-memory processing of batch data jobs like Spark. Flink is considered quite handy when it comes to much iterative processing of the same data items. For machine learning and other use cases that is self-learning, adaptive learning, etc. it is supposed to be an ideal candidate. Still with the rise of IoT technology Flink community also has to face some challenges as well. Some of the considerable advantages of Flink are:
- Better Memory Management: Flink uses explicit memory management that can help in getting rid of occasional spikes, found in the Spark framework
- Actual Stream Processing Engine: It has the capability of batch processing rather than other ones.
- Speed: Faster speed can be managed by it that may be required for iterative processing that has to be taken place either on the same node rather than using several clusters to run them independently. The performance can also be improved by tweaking it to re-process only that data part that is changed not that that is not. It can boost the speed five times as compared to standard processing algorithms.
Difference Between Apache Spark and Apache Flink
|Data Processing||Apache Spark is part of the Hadoop Ecosystem. Basically, it is a batch processing system, but it also supports stream processing.||Flink provides a single runtime for both batch processing and streaming of data functionalities.|
|Streaming engine||Apache Spark processes data in micro-batches. Here each batch contains a collection of event that arrives over the batch period. But for several use cases, sometimes the user may also have to process large data streams to provide real-time results.||Flink is a true streaming engine. The workload streams that are used by Flink are micro-batch, batch, and SQL. Batch is a streamed data finite set.|
|Data Flow||Spark can represent the cyclic data flow as a direct acyclic graph or DAG. However, machine learning algorithms are cyclic data flow.||The approach used by Flink is quite different. A run-time controlled cyclic dependency graph is used in this approach. In this way, machine learning algorithms can be represented efficiently.|
|Memory Management||Spark provides configurable memory management. Now in the latest Spark releases, automatic memory management is offered to the users.||Flink also provides automatic memory management. It uses its own memory management system apart from Java’s garbage collector. For Example Fault Tolerance, Security, Cost, Speed, Latency|
|Fault Tolerance||Apache Spark’s fault tolerance level is quite high, and it can recover the lost work without any additional code and configuration. It always delivers exactly-once semantics.||Flink follows Chandy-Lamport distributed snapshot mechanism to handle fault tolerance. The lightweight mechanism can maintain a high throughput rate and provide guaranteed strong consistency at the same time.|
|Scalability||Spark is a highly scalable framework, and the number of nodes can be continuously kept on adding in any cluster. The largest known Spark cluster has around 8000 nodes.||Flink is also highly scalable, and several nodes can be kept on adding in the cluster. The largest cluster of Flink has around 1000 nodes.|
|Iterative Processing||Spark iterates data in batches. Each of Spark iteration has to be executed and scheduled separately. Spark data can be scheduled for processing so you can leave some of the processes.||Flink data streaming is performed by streaming architecture. Flink can process only some of the data part, especially that has been changed in actual; it can increase the performance significantly.|
|Language Support||Apache Spark supports Scala, Java, Python, and R. Spark is implemented in Scala and provides API in many other popular programming languages including Java, Python, and R.||On the other hand, Flink supports Java, Scala, Python, and R as well, but is implemented in Java. You can also get Scala API too in this.|
|Optimization||The jobs of Apache Scala have to be optimized manually. In the case of MapReduce, some ways are used for this purpose: by using the combiner, configuring the cluster correctly, using LZO compression, by using appropriate writable data type, and tuning the number of MapReduce Task appropriately.||The Apache Flink also comes with an optimizer that is not dependent on the actual programming interface. This optimizer works similarly just like a relational database optimizer, but the optimizers have to be applied on Flink programs rather than SQL queries.|
|Latency||Apache Spark is a relatively faster batch processing system in comparison to MapReduce as much of the input data is caught in memory by RDD, and the intermediate data is kept in memory itself. Here the data is eventually written back on the disk when it completes as and when required.||In the case of Apache Flink, the data streaming runtime is achieved through high throughput and low risk, or we can say low latency.|
|Security||Apache Spark security aspect and feature are a bit sparse and is currently supporting the authentication only through a shared password through the shared secret. If Spark is being run on HDFS, then it can use HDFS ACLs and file-level permissions. Moreover, Kerberos authentication can also be used by Spark if it is running on YARN.||Flink, on the other hand, supports user-authentication via the Hadoop/Kerberos infrastructure. If Flink is being run on YARN, then it can acquire the user’s Kerberos tokens that submit the user program, and then it can authenticate itself at HBase, HDFS, and YARN. The upcoming connector streaming programs of Flink can authenticate themselves via SSL.|
|Cost||Spark may require a lot of RAM to run in-memory, when it is being run in the cluster, its cost increases gradually.||In the case of Apache Flink lot of RAM is required as well to run in-memory, so again the cost of Flink is more than expected.|
Flink does not have large installation numbers as compared to Spark. You can see on Flink website the use of this framework by some of its users that include Alibaba and Capital One. So, it will surely move beyond the beta stage in the future and into the mainstream. However, still, many users are using Spark due to many reasons. It also depends on the requirement and future merits.