Prerequisites and Installation
- You need to install Hive.
- Install Apache Spark from source code (We explain below.) so that you can have a version of Spark without Hive jars already included with it.
- Set HIVE_HOME and SPARK_HOME accordingly.
- Install Hadoop. We do not use it except the Yarn resource scheduler is there and jar files. But Hadoop does not need to be running to use Spark with Hive. ...
Full Answer
Do you need Hive for Spark?
Yes, we can run spark sql queries on spark without installing hive, by default hive uses mapred as an execution engine, we can configure hive to use spark or tez as an execution engine to execute our queries much faster. Hive on spark hive uses hive metastore to run hive queries.
Does Spark come with Hive?
Hive comes bundled with the Spark library as HiveContext, which inherits from SQLContext. Using HiveContext, you can create and find tables in the HiveMetaStore and write queries on it using HiveQL. Users who do not have an existing Hive deployment can still create a HiveContext. When not configured by the hive-site.
How Spark is different than Hive?
Usage: – Hive is a distributed data warehouse platform which can store the data in form of tables like relational databases whereas Spark is an analytical platform which is used to perform complex data analytics on big data.
Does Spark have its own storage?
Even though Spark is said to work faster than Hadoop in certain circumstances, it doesn't have its own distributed storage system.
What can I use instead of Hive?
Top Alternatives to Apache HiveHBase. Apache HBase is an open-source, distributed, versioned, column-oriented store. ... Apache Spark. ... Presto. ... Hadoop. ... Apache Impala. ... Pig. ... Snowflake eliminates the administration and management demands of traditional. ... AWS Glue.
Is Hive being discontinued?
After September 1st 2023 Hive Leak will stop functioning, and by August 1st 2025 all its cameras and security system will have joined it. The sound detection feature of its Hub 360 will disappear at the end of 2022. Hive's customers have begun receiving emails warning them of the shutdown.
Is Spark the best for big data?
The choice depends completely on your business needs. If you are focusing on performance, data compatibility, and ease-of-use, Spark is better than Hadoop. Whereas, Hadoop big data framework is better when you focus on architecture, security, and cost-effectiveness.
Why Hive when we have Spark SQL?
Hive provides schema flexibility, portioning and bucketing the tables whereas Spark SQL performs SQL querying it is only possible to read data from existing Hive installation. Hive provides access rights for users, roles as well as groups whereas no facility to provide access rights to a user is provided by Spark SQL.
Does Spark need JDK or JRE?
To run Spark, you only require a Java runtime environment (JRE) but you may also download the Java development kit (JDK) which includes the JRE. Note that the path to the JRE may be different when using a JDK.
Why Spark is faster than Hive?
In short, it is not a database, but rather a framework which can access external distributed data sets using RDD (Resilient Distributed Data) methodology from data stores like Hive, Hadoop, and HBase. Spark operates quickly because it performs complex analytics in-memory.
Is Spark streaming obsolete?
With that the other older streaming module Spark Streaming is considered obsolete and not used for developing new streaming applications with Apache Spark. Spark Structured Streaming comes with two stream execution engines for executing streaming queries: MicroBatchExecution for Micro-Batch Stream Processing.
Can I run Spark without Hadoop?
You can Run Spark without Hadoop in Standalone Mode Spark and Hadoop are better together Hadoop is not essential to run Spark. If you go by Spark documentation, it is mentioned that there is no need for Hadoop if you run Spark in a standalone mode. In this case, you need resource managers like CanN or Mesos only.
Is there a monthly charge with hive?
Is there a monthly fee for Hive? You do not need to pay monthly fees to use the Hive thermostat. There are extra features via Hive Heating Plus that may incur recurring monthly or yearly fees.
Does Hadoop include Spark?
Apache Spark was written in Scala and is used primarily for machine learning applications. Apache Hadoop is a larger framework that includes utilities such as Apache Spark, Apache Pig, Apache Hive and Apache Phoenix.
Does Hadoop come with Spark?
Need of Hadoop to Run Spark Hadoop and Spark are not mutually exclusive and can work together. Real-time and faster data processing in Hadoop is not possible without Spark. On the other hand, Spark doesn't have any file system for distributed storage.
Is Spark included in PySpark?
Spark is written in Scala, and PySpark was released to support the collaboration of Spark and Python. In addition to providing an API for Spark, PySpark helps you interface with Resilient Distributed Datasets (RDDs) by leveraging the Py4j library. The key data type used in PySpark is the Spark dataframe.
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.
What is a hive?
Remember that Hive is simply a lens for reading and writing HDFS files and not an execution engine in and of itself.
What is the default external catalog implementation?
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.
What is the meaning of "back up"?
Making statements based on opinion; back them up with references or personal experience.
Is hive a data warehouse?
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).
Does Spark SQL use a hive metastore?
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.
Can you use Spark without Hive?
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.
Does hive connect to a hive metastore?
It will connect to a Hive Metastore or instantiate one if none is found when you initialize a HiveContext () object or a spark-shell.
What is hive on spark?
Hive on Spark provides Hive with the ability to utilize Apache Spark as its execution engine. Hive on Spark was added in HIVE-7292.
What is spark.executor.memory?
spark.executor.memory: Amount of memory to use per executor process.
Why allow yarn to cache spark dependency jars on nodes?
Allow Yarn to cache necessary spark dependency jars on nodes so that it does not need to be distributed each time when an application runs.
Does HDFS handle concurrent writers?
Some experiments shows that HDFS client doesn’t handle concurrent writers well, so it may face race condition if executor cores are too many. The following settings need to be tuned for the cluster, these may also apply to submission of Spark jobs outside of Hive on Spark: Property. Recommendation.
How many executors are there in a 9 node cluster?
On this 9 node cluster we’ll have two executors per host. As such we can configure spark.executor.instances somewhere between 2 and 18. A value of 18 would utilize the entire cluster.
Does hive have assembly jar?
Since Hive 2.2.0, Hive on Spark runs with Spark 2.0.0 and above, which doesn't have an assembly jar.
Does Spark include Hive?
Once Spark is installed, find and keep note of the <spark-assembly-*.jar> location. Note that you must have a version of Spark which does not include the Hive jars. Meaning one which was not built with the Hive profile. If you will use Parquet tables, it's recommended to also enable the "parquet-provided" profile.
What is the difference between Hive and Spark?
The main difference between Spark and Hive MapReduce is that while Hive MapReduce converts SQL into Hadoop MapReduce jobs (which write out intermediate stage data to disk), Spark does in-memory computations and transformations. That means you have to do a lot more fine tuning (how much memory, how many executors, etc), but once your job is tuned correct, it’ll run much faster.
What is hive on spark?
Hive on Spark - Hive project that integrates Spark as an additional engine. You can enable this by doing hive.execution.engine=spark. This support was added recently (2015 - 2016).
What is a Spark application?
Spark applications run as separate sets of processes in a cluster, coordinated by the SparkContext object in its main program (called the controller program). Once connected, Spark acquires executors on cluster nodes, which are processes that perform calculations and store data for your application.
Does Spark use hive?
Spark only uses sql interface of hive ie if you want to write sql in spark then you can create a hive context and can use hive sql to put your logic ( alao there is spark sql , which is not that mature till spark 1.6) .
Can hive serialize data?
For Hive you can serialize your data in various ways: Parquet, Avro, etc... but this is only relevant for storage. Hive queries then leverage Map-Reduce to return data.
Is hive on spark better than hive?
Hive on Spark provides all the benefits of Hive and Spark both. Hive was built to be a Data warehousing tool, and now its ability to easily swap execution engines (MR, Tez and Spark) makes it a much more attractive. In a nutshell, with Hive on Spark, your queries are optimized by Hive optimizer and finally a Spark job is executed. All the other Hive features remain intact with a faster and optimized execution engine than the default MR.
Does Spark use Hive MapReduce?
In the Spark SQL set up where we process data in HDFS, Hive MapReduce and Spark SQL depend on Hive Metastore to understand the structure of the data. That means you are usually not choosing between Hive and Spark, but instead would have both Hive and Spark working together on the same cluster. Note that Spark isn’t the only project that is built on top of Hive Metastore, other projects like Presto and Flink can also be deployed on top of Hive Metastore.
