While Apache Hive and Spark SQL perform the same action, retrieving data, each does the task in a different way. Apache Hive: Data operations can be performed using a SQL interface called HiveQL. Applications needing to perform data extraction on huge data sets can employ Spark for faster analytics. Apache Hive: Opinions expressed by DZone contributors are their own. It supports several operating systems. Hive* will probably never support OLTP-type SQL, in which the system updates or modifies a single row at a time, due to limitations of the underlying Apache* Hadoop* Distributed File System. Any Hive query can easily be executed in Spark SQL but vice-versa is not true. Spark can pull data from any data store running on Hadoop and perform complex analytics in-memory and in-parallel. To ke… Published at DZone with permission of Daniel Berman, DZone MVB. Also discussed complete discussion of Apache Hive vs Spark SQL. Both Apache Hiveand Impala, used for running queries on HDFS. Spark SQL Interview Questions. At First, we have to write complex Map-Reduce jobs. However, Apache Pig works faster than Apache Hive. As mentioned earlier, advanced data analytics often need to be performed on massive data sets. Why Spark? Hive is not an option for unstructured data. Hive is a specially built database for data warehousing operations, especially those that process terabytes or petabytes of data. Spark SQL supports only JDBC and ODBC. Required fields are marked *, Home About us Contact us Terms and Conditions Privacy Policy Disclaimer Write For Us Success Stories, This site is protected by reCAPTCHA and the Google. Spark applications can run up to 100x faster in terms of memory and 10x faster in terms of disk computational speed than Hadoop. Spark claims to run 100 times faster than MapReduce. For example, if it takes 5 minutes to execute a query in Hive then in Spark SQL it will take less than half a minute to execute the same query. It can run on thousands of nodes and can make use of commodity hardware. There is a selectable replication factor for redundantly storing data on multiple nodes. This time, instead of reading from a file, we will try to read from a Hive SQL table. Spark SQL provides faster execution than Apache Hive. This data is mainly generated from system servers, messaging applications, etc. Overall the user should find Hive-LLAP and Hive on MR3 running much faster than Spark SQL for typical queries. Furthermore, Apache Hive has better access choices and features than that in Apache Pig. Apache Hive: Spark SQL: But there are some differences between Hive and Impala – SQL war in the Hadoop Ecosystem. 1) Explain the difference between Spark SQL and Hive. Performance and scalability quickly became issues for them, since RDBMS databases can only scale vertically. Spark SQL: Your email address will not be published. As JDBC/ODBC drivers are available in Hive, we can use it. We can use several programming languages in Hive. At the time, Facebook loaded their data into RDBMS databases using Python. Join the DZone community and get the full member experience. Spark SQL: Currently released on 24 October 2017: version 2.3.1 Apache Hive was first released in 2012. It is open sourced, from Apache Version 2. Apache Hive: So, when Hadoop was created, there were only two things. Spark was introduced as an alternative to MapReduce, a slow and resource-intensive programming model. This presentation was given at the Strata + Hadoop World, 2015 in San Jose. In short, it is not a database, but rather a framework that can access external distributed data sets using an RDD (Resilient Distributed Data) methodology from data stores like Hive, Hadoop, and HBase. The data sets can also reside in the memory until they are consumed. Spark SQL: Apache Hive is the de facto standard for SQL-in-Hadoop. Your email address will not be published. Also, there are several limitations with Hive as well as SQL. Hive brings in SQL capability on top of Hadoop, making it a horizontally scalable database and a great choice for DWH environments. It achieves this high performance by performing intermediate operations in memory itself, thus reducing the number of read and writes operations on disk. For example Linux OS, X, and Windows. Hive is similar to an RDBMS database, but it is not a complete RDBMS. Also, gives information on computations performed. Hive does not support online transaction processing. May 9, 2019. Spark which has been proven much faster than map reduce eventually had to support hive. This makes Hive a cost-effective product that renders high performance and scalability. Building a Hadoop career is everyone’s dream in today’s IT industry. Spark SQL: Again, using git to control project. However, what I see in the industry( Uber , Neflix examples) Presto is used as ad-hock SQL analytics whereas Spark … Hive Architecture is quite simple. The data is pulled into the memory in-parallel and in chunks. For Example, float or date. This reduces data shuffling and the execution is optimized. Primarily, its database model is Relational DBMS. Hive is a pure data warehousing database that stores data in the form of tables. Hive is basically a front ... Why Is Impala Faster Than Hive? Also, SQL makes programming in spark easier. At the time of writing this article, the latest stable version of Spark SQL is 2.4.4. Spark SQL: In Apache Hive, latency for queries is generally very high. Hive comes with enterprise-grade features and capabilities that can help organizations build efficient, high-end data warehousing solutions. Basically, we can implement Apache Hive on Java language. Hadoop is more cost effective processing massive data sets. This creates difference between SparkSQL and Hive. Hive on Spark provides us right away all the tremendous benefits of Hive and Spark both. For example Java, Python, R, and Scala. Hence, if you’re already familiar with SQL but not a programmer, this blog might have shown you … Apache Hive: Spark SQL: However, Hive is planned as an interface or convenience for querying data stored in HDFS. Basically, for redundantly storing data on multiple nodes, there is a no replication factor in Spark SQL. Hive is an open-source distributed data warehousing database that operates on Hadoop Distributed File System. As same as Hive, Spark SQL also support for making data persistent. It uses spark core for storing data on different nodes. Through Spark SQL, it is possible to read data from existing Hive installation. Afterwards, we will compare both on the basis of various features. Spark SQL is faster than Hive when it comes to processing speed. See the original article here. Apart from it, we have discussed we have discussed Usage as well as limitations above. Hadoop is a distributed file system (HDFS) while Spark is a compute engine running on top of Hadoop or your local file system. Apache Spark utilizes RAM and isn’t tied to Hadoop’s two-stage paradigm. Apache Hive: All the same, in Spark 2.0 Spark SQL tuned to be a main API. Hive is originally developed by Facebook. Whereas, spark SQL also supports concurrent manipulation of data. Benchmarks performed at UC Berkeley’s Amplab show that Spark runs much faster than Tez (the tests refer to Spark as Shark, which is the predecessor to Spark SQL). Also provides acceptable latency for interactive data browsing. Hive is the best option for performing data analytics on large volumes of data using SQL. Spark SQL: Spark SQL places first only for three queries (query 30, 41, and 81). Hive and Spark are both immensely popular tools in the big data world. Spark SQL: Then, the resulting data sets are pushed across to their destination. And Spark RDD now is just an internal implementation of it. Note: LLAP is much more faster than any other execution engines. Users who are comfortable with SQL, Hive is mainly targeted towards them. The core reason for choosing Hive is because it is a SQL interface operating on Hadoop. In Spark, we use Spark SQL for structured data processing. Hive and Spark are different products built for different purposes in the big data space. Hive (which later became Apache) was initially developed by Facebook when they found their data growing exponentially from GBs to TBs in a matter of days. Apache Spark is potentially 100 times faster than Hadoop MapReduce. Though, MySQL is planned for online operations requiring many reads and writes. Spark operates quickly because it performs complex analytics in-memory. Apache Hive: Apache Hive had certain limitations as mentioned below. Apache Hive is the most popular and most widely used SQL solution for Hadoop. Hive can now be accessed and processed using spark SQL jobs. They needed a database that could scale horizontally and handle really large volumes of data. Spark SQL: It possesses SQL-like DML and DDL statements. Moreover, It is an open source data warehouse system. You have learned that Spark SQL is like HIVE but faster. Spark: Apache Spark processes faster than MapReduce because it caches much of the input data on memory by RDD and keeps intermediate data in memory itself, eventually writes the data to disk upon completion or whenever required. Hive is the best option for performing data analytics on large volumes of … So, in this article, “Impala vs Hive” we will compare Impala vs Hive performance on the basis of different features and discuss why Impala is faster than Hive, when to use Impala vs hive. Spark Streaming is an extension of Spark that can live-stream large amounts of data from heavily-used web sources. Apache Spark is now more popular that Hadoop MapReduce. Apache Hive: At first, we will put light on a brief introduction of each. Apache Hive: In addition, Hive is not ideal for OLTP or OLAP operations. Spark pulls data from the data stores once, then performs analytics on the extracted data set in-memory, unlike other applications that perform analytics in databases. Spark however is faster than MapReduce which was the first compute engine created when HDFS was created. It has predefined data types. Spark SQL: Spark SQL originated as Apache Hive to run on top of Spark and is now integrated with the Spark stack. Impala (“SQL on HDFS”) : Why Impala query speed is faster than Hive? In addition, it reduces the complexity of MapReduce frameworks. Spark SQL, users can selectively use SQL constructs to write queries for Spark pipelines. Apache Hive: This capability reduces Disk I/O and network contention, making it ten times or even a hundred times faster. We will also cover the features of both individually. This article focuses on describing the history and various features of both products. Moreover, We get more information of the structure of data by using SQL. Faster Execution - Spark SQL is faster than Hive. It provides a faster, more modern alternative to MapReduce. Spark SQL: Hive is a distributed database, and Spark is a framework for data analytics. Key-value store Because of its support for ANSI SQL standards, Hive can be integrated with databases like HBase and Cassandra. Though, MySQL is planned for online operations requiring many reads and writes. Apache Hive: Apache Hive: Over a million developers have joined DZone. ), we were intrigued by the reports that the optimizations built into the DataFrames make it comparable in speed to the usual Spark RDD API, which in turn is well known to be much faster than … First of all, Spark is not faster than Hadoop. Although, Interaction with Spark SQL is possible in several ways. Hive can also be integrated with data streaming tools such as Spark, Kafka, and Flume. Apache Hive: We get the result as Dataset/DataFrame if we run Spark SQL with another programming language. Hive helps perform large-scale data analysis for businesses on HDFS, making it a horizontally scalable database. It is specially built for data warehousing operations and is not an option for OLTP or OLAP. It has emerged as a top level Apache project. Why is Spark SQL used? Lastly, Spark has its own SQL, Machine Learning, Graph and Streaming components unlike Hadoop, where you have to install all the other frameworks separately and data movement between these frameworks is a nasty job. Hive and Spark are both immensely popular tools in the big data world. Although, we can just say it’s usage is totally depends on our goals. One can achieve extra optimization in Apache Spark, with this extra information. With the massive amount of increase in big data technologies today, it is becoming very important to use the right tool for every process. Tags: Spark sql vs hive on sparkSparkSQL vs Hive. Because of its ability to perform advanced analytics, Spark stands out when compared to other data streaming tools like Kafka and Flume. So we will discuss Apache Hive vs Spark SQL on the basis of their feature. While, Hive’s ability to switch execution engines, is efficient to query huge data sets. We can implement Spark SQL on Scala, Java, Python as well as R language. A comparison of their capabilities will illustrate the various complex data processing problems these two products can address. However, Hive is planned as an interface or convenience for querying data stored in HDFS. The process can be anything like Data ingestion, … There are access rights for users, groups as well as roles. Published on ... Two Fundamental Changes in Apache Spark. Basically, it supports all Operating Systems with a Java VM. Spark SQL: Hence, we can not say SparkSQL is not a replacement for Hive neither is the other way. Spark SQL: In general, it is hard to say if Presto is definitely faster or slower than Spark SQL. The core strength of Spark is its ability to perform complex in-memory analytics and stream data sizing up to petabytes, making it more efficient and faster than MapReduce. 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