What is a Metastore?
Metastore – The component that stores all the structure information of the various tables and partitions in the warehouse including column and column type information, the serializers and deserializers necessary to read and write data and the corresponding HDFS files where the data is stored.
What is in a spark Hive Metastore?
A Hive metastore warehouse (aka spark-warehouse) is the directory where Spark SQL persists tables whereas a Hive metastore (aka metastore_db) is a relational database to manage the metadata of the persistent relational entities, e.g. databases, tables, columns, partitions.
What is metadata spark?
Metadata is a wrapper over Map[String, Any] that limits the value type to simple ones: Boolean, Long, Double, String, Metadata, Array[Boolean], Array[Long], Array[Double], Array[String], and Array[Metadata]. JSON is used for serialization. The default constructor is private.
What is metadata and Metastore?
Metastore is the central repository of Apache Hive metadata. It stores metadata for Hive tables (like their schema and location) and partitions in a relational database. It provides client access to this information by using metastore service API.
Why do we need Metastore for Hive?
Because it stores all the information about the structure of our data and its location. This is the reason why many big companies are using it, to good effect. We're well aware that many of our customers are working with Hive Metastore or its Amazon implementation, Glue Data Catalog.
Is Hive Metastore a database?
The Hive metastore is simply a relational database. It stores metadata related to the tables/schemas you create to easily query big data stored in HDFS.
What is the purpose of metadata?
Metadata helps understand the data behind it and reflects how data is used and is key to supporting data governance initiatives, regulatory compliance demands, and data management processes. It is critical to data management because it provides essential details about an organization's data assets: What is that data.
What exactly metadata means?
Data that provide information about other data. Metadata summarizes basic information about data, making finding & working with particular instances of data easier. Metadata can be created manually to be more accurate, or automatically and contain more basic information.
What are the three types of metadata?
There are three main types of metadata: descriptive, administrative, and structural.
What are the five types of metadata?
The subtle 6: Types of metadata you need to knowDescriptive metadata. Descriptive metadata is, in its most simplified version, an identification of specific data. ... Structural metadata. ... Preservation metadata. ... Provenance metadata. ... Use metadata. ... Administrative metadata.
How do you create a Metastore?
Click Create Metastore. Enter a name for the metastore. Enter the region where the metastore will be deployed. This must be the same region as the root storage account and the workspaces you want to use to access the data.
What is the difference between Meta and metadata?
Definition and Usage The tag defines metadata about an HTML document. Metadata is data (information) about data. tags always go inside the
element, and are typically used to specify character set, page description, keywords, author of the document, and viewport settings.How do I access Hive Metastore from Spark?
to connect to hive metastore you need to copy the hive-site. xml file into spark/conf directory. After that spark will be able to connect to hive metastore.
What is stored in Spark user memory?
Memory usage in Spark largely falls under one of two categories: execution and storage. Execution memory refers to that used for computation in shuffles, joins, sorts and aggregations, while storage memory refers to that used for caching and propagating internal data across the cluster.
What are the components of a DataFrame in Spark?
In Spark, DataFrames are the distributed collections of data, organized into rows and columns. Each column in a DataFrame has a name and an associated type. DataFrames are similar to traditional database tables, which are structured and concise.
What are key components of Spark?
Apache Spark consists of Spark Core Engine, Spark SQL, Spark Streaming, MLlib, GraphX, and Spark R. You can use Spark Core Engine along with any of the other five components mentioned above. It is not necessary to use all the Spark components together.
What is spark.sql.warehouse.dir?
spark.sql.warehouse.dir is a static configuration property that sets Hive’s hive.metastore.warehouse.dir property, i.e. the location of default database for the Hive warehouse.
What does SparkSession do when not configured?
When not configured by the hive-site.xml, SparkSession automatically creates metastore_db in the current directory and creates a directory configured by spark.sql. warehouse.dir, which defaults to the directory spark-warehouse in the current directory that the Spark application is started.
Why is embedded deployment mode not recommended?
The default embedded deployment mode is not recommended for production use due to limitation of only one active SparkSession at a time.
Can you access the connection properties for a hive metastore in a Spark SQL application?
You can access the current connection properties for a Hive metastore in a Spark SQL application using the Spark internal classes.
Is hive.metastore.warehouse.dir deprecated?
hive.metastore.warehouse.dir property in hive-site.xml is deprecated since Spark 2.0.0. Use spark.sql.warehouse.dir to specify the default location of the databases in a Hive warehouse.
Specifying storage format for Hive tables
When you create a Hive table, you need to define how this table should read/write data from/to file system, i.e. the “input format” and “output format”. You also need to define how this table should deserialize the data to rows, or serialize rows to data, i.e. the “serde”.
Interacting with Different Versions of Hive Metastore
One of the most important pieces of Spark SQL’s Hive support is interaction with Hive metastore, which enables Spark SQL to access metadata of Hive tables. Starting from Spark 1.4.0, a single binary build of Spark SQL can be used to query different versions of Hive metastores, using the configuration described below.
