Does Spark SQL use Hive Metastore?

Question

I am developing a Spark SQL application and I've got few questions:

1. I read that Spark-SQL uses Hive metastore under the cover? Is this true? I'm talking about a pure Spark-SQL application that does not explicitly connect to any Hive installation.
2. I am starting a Spark-SQL application, and have no need to use Hive. Is there any reason to use Hive? From what I understand Spark-SQL is much faster than Hive; so, I don't see any reason to use Hive. But am I correct?

Answer 1

I read that Spark-SQL uses Hive metastore under the cover? Is this true? I'm talking about a pure Spark-SQL application that does not explicitly connect to any Hive installation.

Spark SQL does not use a Hive metastore under the covers (and defaults to in-memory non-Hive catalogs unless you're in spark-shell that does the opposite).

The default external catalog implementation is controlled by spark.sql.catalogImplementation internal property and can be one of the two possible values: hive and in-memory.

Use the SparkSession to know what catalog is in use.

scala> :type spark
org.apache.spark.sql.SparkSession

scala> spark.version
res0: String = 2.4.0

scala> :type spark.sharedState.externalCatalog
org.apache.spark.sql.catalyst.catalog.ExternalCatalogWithListener

scala> println(spark.sharedState.externalCatalog.unwrapped)
org.apache.spark.sql.hive.HiveExternalCatalog@49d5b651

Please note that I used spark-shell that does start a Hive-aware SparkSession and so I had to start it with --conf spark.sql.catalogImplementation=in-memory to turn it off.

I am starting a Spark-SQL application, and have no need to use Hive. Is there any reason to use Hive? From what I understand Spark-SQL is much faster than Hive; so, I don't see any reason to use Hive.

That's a very interesting question and can have different answers (some even primarily opinion-based so we have to be extra careful and follow the StackOverflow rules).

Is there any reason to use Hive?

No.

But...if you want to use the very recent feature of Spark 2.2, i.e. cost-based optimizer, you may want to consider it as ANALYZE TABLE for cost statistics can be fairly expensive and so doing it once for tables that are used over and over again across different Spark application runs could give a performance boost.

Please note that Spark SQL without Hive can do it too, but have some limitation as the local default metastore is just for a single-user access and reusing the metadata across Spark applications submitted at the same time won't work.

I don't see any reason to use Hive.

I wrote a blog post Why is Spark SQL so obsessed with Hive?! (after just a single day with Hive) where I asked a similar question and to my surprise it's only now (almost a year after I posted the blog post on Apr 9, 2016) when I think I may have understood why the concept of Hive metastore is so important, esp. in multi-user Spark notebook environments.

Hive itself is just a data warehouse on HDFS so not much use if you've got Spark SQL, but there are still some concepts Hive has done fairly well that are of much use in Spark SQL (until it fully stands on its own legs with a Hive-like metastore).