PySpark FAQ

About PySpark Local Installation

Can I install PySpark in Windows 10?

Yes, PySpark can be installed in Windows 10 or even earlier version of it, refer complete guide here.

General PySpark Queries

How does PySpark relate to Hadoop/MapReduce?

PySpark/Saprk is a fast and general processing compuete engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS

Who is using Spark in production?

As of 2019, surveys show that more than 5000+ organizations are using Spark in production.

How large a cluster can Spark scale to?

Many organizations run Spark on clusters of thousands of nodes. The largest cluster we know has 8000 of them.

Does my data need to fit in memory to use Spark?

No. PySpark engine spill data to disk if it does not fit in memory, allowing it to run well on any sized data.

How can I run Spark on a cluster?

You can use either the standalone deploy mode, which only needs Java to be installed on each node, or the Mesos and YARN cluster managers.

Note that you can also run Spark locally (possibly on multiple cores) without any special setup by just passing local[N] as the master URL, where N is the number of parallel threads you want.

Do I need Hadoop to run Spark?

No, but if you run on a cluster, you will need some form of shared file system (for example, NFS mounted at the same path on each node). If you have this type of filesystem, you can just deploy Spark in standalone mode.

Does Spark require modified versions of Scala or Python?

No. Spark requires no changes to Scala or compiler plugins. The Python API uses the standard CPython implementation, and can call into existing C libraries for Python such as NumPy.

I understand Spark Streaming uses micro-batching.

While Spark does use a micro-batch execution model, this does not have much impact on applications, because the batches can be as short as 0.5 seconds.