Spark SQL: Hadoop is a distributed file system (HDFS) while Spark is a compute engine running on top of Hadoop or your local file system. Apache Hive: Also provides acceptable latency for interactive data browsing. Though, MySQL is planned for online operations requiring many reads and writes. Spark can pull data from any data store running on Hadoop and perform complex analytics in-memory and in-parallel. Basically, it supports all Operating Systems with a Java VM. As similar as Hive, it also supports Key-value store as additional database model. Published at DZone with permission of Daniel Berman, DZone MVB. This data is mainly generated from system servers, messaging applications, etc. Tags: Spark sql vs hive on sparkSparkSQL vs Hive. The data sets can also reside in the memory until they are consumed. As JDBC/ODBC drivers are available in Hive, we can use it. It is open sourced, from Apache Version 2. Spark Streaming is an extension of Spark that can live-stream large amounts of data from heavily-used web sources. Apache Hive is the de facto standard for SQL-in-Hadoop. There is a selectable replication factor for redundantly storing data on multiple nodes. It has emerged as a top level Apache project. Users who are comfortable with SQL, Hive is mainly targeted towards them. 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. Hive and Spark are both immensely popular tools in the big data world. It is not mandatory to create a metastore in Spark SQL but it is mandatory to create a Hive metastore. Spark SQL: It has predefined data types. For example C++, Java, PHP, and Python. This makes Hive a cost-effective product that renders high performance and scalability. Let’s see few more difference between Apache Hive vs Spark SQL. HiveQL is a SQL engine that helps build complex SQL queries for data warehousing type operations. 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. As mentioned earlier, advanced data analytics often need to be performed on massive data sets. Published on ... Two Fundamental Changes in Apache Spark. 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. Spark extracts data from Hadoop and performs analytics in-memory. Spark not only supports MapReduce, but it also supports SQL-based data extraction. Hive and Spark are two very popular and successful products for processing large-scale data sets. Hadoop was already popular by then; shortly afterward, Hive, which was built on top of Hadoop, came along. While, Hive’s ability to switch execution engines, is efficient to query huge data sets. Hence, we can not say SparkSQL is not a replacement for Hive neither is the other way. Also, helps for analyzing and querying large datasets stored in Hadoop files. Apache Hive’s logo. 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. Note: ANSI SQL-92 is the third revision of the SQL database query language. Spark SQL places first only for three queries (query 30, 41, and 81). Spark streaming is an extension of Spark that can stream live data in real-time from web sources to create various analytics. For example, float or date. The process can be anything like Data ingestion, … This blog totally aims at differences between Spark SQL vs Hive in Apache Spar… Spark SQL supports real-time data processing. Though there are other tools, such as Kafka and Flume that do this, Spark becomes a good option performing really complex data analytics is necessary. Also, can portion and bucket, tables in Apache Hive. Building a Hadoop career is everyone’s dream in today’s IT industry. Hive is basically a front ... Why Is Impala Faster Than Hive? 1) Explain the difference between Spark SQL and Hive. Hive and Spark are both immensely popular tools in the big data world. It really depends on the type of query you’re executing, environment and engine tuning parameters. Spark SQL:   Is Spark SQL faster than Hive? Faster Execution - Spark SQL is faster than Hive. Apache Spark * An open source, Hadoop-compatible, fast and expressive cluster-computing platform. Spark SQL is a library whereas Hive is a framework. Afterwards, we will compare both on the basis of various features. Because of its ability to perform advanced analytics, Spark stands out when compared to other data streaming tools like Kafka and Flume. Hive helps perform large-scale data analysis for businesses on HDFS, making it a horizontally scalable database. Here is a quick summary of this video. In theory swapping out engines (MR, TEZ, Spark) should be easy. Currently released on 24 October 2017:  version 2.3.1 Your email address will not be published. For Example, float or date. Hive is a pure data warehousing database that stores data in the form of tables. Apache Hive is built on top of Hadoop. This article focuses on describing the history and various features of both products. Indeed, Shark is compatible with Hive. It uses data sharding method for storing data on different nodes. Spark SQL: 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). Like Apache Hive, it also possesses SQL-like DML and DDL statements. If you are already heavily invested in the Hive ecosystem in terms of code and skills I would look at Hive on Spark as my engine. Spark SQL vs. Hive QL- Advantages of Spark SQL over HiveQL. Applications needing to perform data extraction on huge data sets can employ Spark for faster analytics. Spark SQL: Don't become Obsolete & get a Pink Slip As same as Hive, Spark SQL also support for making data persistent. It is specially built for data warehousing operations and is not an option for OLTP or OLAP. Also, there are several limitations with Hive as well as SQL. Apache Hive: 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. Apache Hive had certain limitations as mentioned below. Yes, SparkSQL is much faster than Hive, especially if it performs only in-memory computations, but Impala is still faster than SparkSQL. Spark was introduced as an alternative to MapReduce, a slow and resource-intensive programming model. Furthermore, Apache Hive has better access choices and features than that in Apache Pig. We can implement Spark SQL on Scala, Java, Python as well as R language. See the original article here. In other words, they do big data analytics. First of all, Spark is not faster than Hadoop. Over a million developers have joined DZone. There are access rights for users, groups as well as roles. Although, we can just say it’s usage is totally depends on our goals. Spark SQL: And Spark RDD now is just an internal implementation of it. Spark SQL: Although, Interaction with Spark SQL is possible in several ways. The data is stored in the form of tables (just like a RDBMS). At the time, Facebook loaded their data into RDBMS databases using Python. It achieves this high performance by performing intermediate operations in memory itself, thus reducing the number of read and writes operations on disk. It uses spark core for storing data on different nodes. Apache Hive:   There are no access rights for users. Hive is an open-source distributed data warehousing database that operates on Hadoop Distributed File System. Spark SQL: It can also extract data from NoSQL databases like MongoDB. It is originally developed by Apache Software Foundation. Spark supports different programming languages like Java, Python, and Scala that are immensely popular in big data and data analytics spaces. At first, we will put light on a brief introduction of each. So, when Hadoop was created, there were only two things. 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. 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. Hive and Spark are different products built for different purposes in the big data space. This blog totally aims at differences between Spark SQL vs Hive in Apache Spark. They needed a database that could scale horizontally and handle really large volumes of data. All the same, in Spark 2.0 Spark SQL tuned to be a main API. These tools have limited support for SQL and can help applications perform analytics and report on larger data sets. Apache Hive: Conclusion. Moreover, It is an open source data warehouse system. 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