What does in-memory processing means?

In-memory processing is the practice of taking action on data entirely in computer memory (e.g., in RAM). This is in contrast to other techniques of processing data which rely on reading and writing data to and from slower media such as disk drives.

What are the advantages of in-memory processing?

The biggest advantage of in-memory processing is speed. Working from RAM or flash memory removes many of the bottlenecks found in disk-based processing. Thus, businesses are able to analyse large datasets in real-time, which generates better insights from data analytics.

How does cache memory work in spark?

Spark will cache whatever it can in memory and spill the rest to disk. Reading data from source(hdfs:// or s3://) is time consuming. So after you read data from the source and apply all the common operations, cache it if you are going to reuse the data.

Is spark in memory database?

Spark being a processing framework is not a database or filesystem, albeit offering drivers to many databases and filesystems. It offers in-memory storage with a seamless integration with Spark. If several Spark jobs are accessing the same dataset stored in Tachyon, the dataset is not replicated but loaded only once.

How does spark deal with memory problems?

To fix this error we need to set the partition size with below configuration setting. GC Overhead limit exceeded. — Increase executor memory. At times we also need to check if the value for spark.

Why do we need in memory analytics?

For many organizations, the key benefit of in-memory analytics is the ability to process vast quantities of data fast enough so that the resulting insights become a difference maker. Pattern recognition involving large amounts of data is a key use case.

Which is better cache or persist?

Spark Cache vs Persist Both caching and persisting are used to save the Spark RDD, Dataframe, and Dataset’s. But, the difference is, RDD cache() method default saves it to memory (MEMORY_ONLY) whereas persist() method is used to store it to the user-defined storage level.