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Apache Spark and Apache Flink are both open-sourced, distributed processing framework, which was built to reduce the latencies of Hadoop Mapreduce in fast data processing. There is a common conception that Flink is going to replace Spark. However, the reality is different. So rather than discussing which one would topple the other, let's talk about the advantages each of the framework has.
Advantages of Spark
Apache Spark has several advantages over traditional Big Data and MapReduce-based technologies. The prominent ones are. It essentially takes MapReduce to the next level with a performance that is several times faster. One of the key differentiators for Spark is its ability to hold intermediate results in-memory itself, rather than writing back to disk and reading from it again, which is critical for iteration based use cases.
Speed: Spark can execute batch processing jobs 10 to 100 times faster than MapReduce. That doesn’t mean it lags behind when data has to be written to (and fetched from) disk, as it is the world record holder for large-scale on-disk sorting.
Ease Handling: Apache Spark has easy to use APIs, built for operating on large datasets.
Unified Engine: Spark can run on top of Hadoop, making use of its cluster manager and underlying storage. However, it can also run independent of Hadoop, joining hands with other cluster managers and storage platforms. It also comes with higher – level libraries that support SQL queries data streaming, machine learning and graph processing.
In-memory data sharing: Different jobs can share data within the memory, which makes it an ideal choice for iterative, interactive and event stream processing tasks.
Expanding user community: An active user community has led to a stable release of Spark within 2 years of its initial release. This speaks volumes of its worldwide acceptability, which is on the rise.
German for ‘quick’ or ‘nimble’, Apache Flink is the latest entrant to the list of open-source frameworks focused on Big Data Analytics that are trying to replace Hadoop’s aging MapReduce, just like Spark.
Advantages of Flink
Actual stream processing engine that can approximate batch processing, rather than being the other way around.
Better memory management: Explicit memory management gets rid of the occasional spikes found in Spark framework.
Speed: It manages faster speeds by allowing iterative processing to take place on the same node rather than having the cluster run them independently. Its performance can be further tuned by tweaking it to re-process only that part of data that has changed rather than the entire set. It offers up to five-fold boost in speed when compared to the standard processing algorithm.
Less configuration: While Spark needs a lot of configuration to be done, Flink gets easily configured.