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. 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). It does not offer real-time queries and row level updates. All the same, in Spark 2.0 Spark SQL tuned to be a main API. It is not mandatory to create a metastore in Spark SQL but it is mandatory to create a Hive metastore. Hive is a specially built database for data warehousing operations, especially those that process terabytes or petabytes of data. Apache Hive:   In Spark, we use Spark SQL for structured data processing. The data sets can also reside in the memory until they are consumed. Spark can pull the data from any data store running on Hadoop and perform complex analytics in-memory and in parallel. Spark Architecture can vary depending on the requirements. 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. One can achieve extra optimization in Apache Spark, with this extra information. The core reason for choosing Hive is because it is a SQL interface operating on Hadoop. Here is a quick summary of this video. It possesses SQL-like DML and DDL statements. There are access rights for users, groups as well as roles. While Apache Spark SQL was first released in 2014. It uses data sharding method for storing data on different nodes. Hive on Spark provides us right away all the tremendous benefits of Hive and Spark both. For example Java, Python, R, and Scala. Your email address will not be published. Basically, for redundantly storing data on multiple nodes, there is a no replication factor in Spark SQL. Hive brings in SQL capability on top of Hadoop, making it a horizontally scalable database and a great choice for DWH environments. It uses spark core for storing data on different nodes. It uses in-memory computation where the time required to move data in and out of a disk is lesser when compared to Hive. Apache Spark is now more popular that Hadoop MapReduce. Published on ... Two Fundamental Changes in Apache Spark. Spark SQL is faster than Hive. Hive can also be integrated with data streaming tools such as Spark, Kafka, and Flume. It has emerged as a top level Apache project. 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. In Apache Hive, latency for queries is generally very high. In other words, they do big data analytics. Also, helps for analyzing and querying large datasets stored in Hadoop files. Apache Spark * An open source, Hadoop-compatible, fast and expressive cluster-computing platform. Spark SQL: Also, SQL makes programming in spark easier. 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. Spark is 100 times faster than MapReduce and this shows how Spark is better than Hadoop MapReduce. Spark, on the other hand, is the best option for running big data analytics. This blog totally aims at differences between Spark SQL vs Hive in Apache Spar… Moreover, We get more information of the structure of data by using SQL. Spark SQL: As JDBC/ODBC drivers are available in Hive, we can use it. At First, we have to write complex Map-Reduce jobs. Apart from it, we have discussed we have discussed Usage as well as limitations above. Don't become Obsolete & get a Pink Slip Moreover, It is an open source data warehouse system. Spark extracts data from Hadoop and performs analytics in-memory. This creates difference between SparkSQL and Hive. Impala is faster and handles bigger volumes of data than Hive query engine. Spark SQL: So, hopefully, this blog may answer all the questions occurred in mind regarding Apache Hive vs Spark SQL. It can also extract data from NoSQL databases like MongoDB. It supports an additional database model, i.e. So we will discuss Apache Hive vs Spark SQL on the basis of their feature. As mentioned earlier, it is a database that scales horizontally and leverages Hadoop’s capabilities, making it a fast-performing, high-scale database. Spark can be integrated with various data stores like Hive and HBase running on Hadoop. Published at DZone with permission of Daniel Berman, DZone MVB. Spark SQL: Also, there are several limitations with Hive as well as SQL. Although, we can just say it’s usage is totally depends on our goals. Though SQL-like query engines on non-SQL data stores is not a new concept (c.f., Hive, Shark, etc. At the time of writing this article, the latest stable version of Spark SQL is 2.4.4. Hive is not an option for unstructured data. 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. 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. Hive is the best option for performing data analytics on large volumes of data using SQL. Spark SQL: Any Hive query can easily be executed in Spark SQL but vice-versa is not true. At the time, Facebook loaded their data into RDBMS databases using Python. 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. Is Spark SQL faster than Hive? 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. Spark SQL: Both Apache Hiveand Impala, used for running queries on HDFS. It is originally developed by Apache Software Foundation. May 9, 2019. For Example, float or date. There are no access rights for users. Spark SQL:   Spark SQL vs. Hive QL- Advantages of Spark SQL over HiveQL. Again, using git to control project. Apache Spark utilizes RAM and isn’t tied to Hadoop’s two-stage paradigm. Hive is basically a front ... Why Is Impala Faster Than Hive? Apache Hive: Apache Hive: Whereas, spark SQL also supports concurrent manipulation of data. Although, Interaction with Spark SQL is possible in several ways. These tools have limited support for SQL and can help applications perform analytics and report on larger data sets. Over a million developers have joined DZone. Hive is a pure data warehousing database that stores data in the form of tables. Spark SQL: Apache Hive: It has predefined data types. Primarily, its database model is Relational DBMS. Currently released on 09 October 2017: version 2.1.2. While, Hive’s ability to switch execution engines, is efficient to query huge data sets. Hence, if you’re already familiar with SQL but not a programmer, this blog might have shown you … For example, float or date. Spark which has been proven much faster than map reduce eventually had to support hive. So we will discuss Apache Hive vs Spark SQL on the basis of their feature. Spark not only supports MapReduce, but it also supports SQL-based data extraction. Spark has its own SQL engine and works well when integrated with Kafka and Flume. Join the DZone community and get the full member experience. Also discussed complete discussion of Apache Hive vs Spark SQL. Hive Architecture is quite simple. Also, gives information on computations performed. Spark Streaming is an extension of Spark that can live-stream large amounts of data from heavily-used web sources. And all top level libraries are being re-written to work on data frames. Applications needing to perform data extraction on huge data sets can employ Spark for faster analytics. Spark operates quickly because it performs complex analytics in-memory. Hive was built for querying and analyzing big data. This capability reduces Disk I/O and network contention, making it ten times or even a hundred times faster. I spent the whole yesterday learning Apache Hive.The reason was simple — Spark SQL is so obsessed with Hive that it offers a dedicated HiveContext to work with Hive (for HiveQL queries, Hive metastore support, user-defined functions (UDFs), SerDes, ORC file format support, etc.) Why Spark? It provides a faster, more modern alternative to MapReduce. Data operations can be performed using a SQL interface called HiveQL. However, what I see in the industry( Uber , Neflix examples) Presto is used as ad-hock SQL analytics whereas Spark … Such as DataFrame and the Dataset API. It is an RDBMS-like database, but is not 100% RDBMS. At first, we will put light on a brief introduction of each. Follow DataFlair on Google News & Stay ahead of the game. For example Linux OS, X,  and Windows. Hive helps perform large-scale data analysis for businesses on HDFS, making it a horizontally scalable database. It is open sourced, from Apache Version 2. HiveQL is a SQL engine that helps build complex SQL queries for data warehousing type operations. Key-value store Spark SQL: It is specially built for data warehousing operations and is not an option for OLTP or OLAP. Why is Spark SQL used? And Spark RDD now is just an internal implementation of it. While Apache Hive and Spark SQL perform the same action, retrieving data, each does the task in a different way. But later donated to the Apache Software Foundation, which has maintained it since. Spark supports different programming languages like Java, Python, and Scala that are immensely popular in big data and data analytics spaces. In general, it is hard to say if Presto is definitely faster or slower than Spark SQL. Apache Hive’s logo. While Apache Hive and Spark SQL perform the same action, retrieving data, each does the task in a different way. Hive does not support online transaction processing. Apache Hive: Spark SQL: Although, no provision of error for oversize of varchar type. Spark has an answer to Hive called Shark that allows you to run SQL queries on Spark data. Because of its support for ANSI SQL standards, Hive can be integrated with databases like HBase and Cassandra. They needed a database that could scale horizontally and handle really large volumes of data. Hive and Spark are both immensely popular tools in the big data world. Spark SQL originated as Apache Hive to run on top of Spark and is now integrated with the Spark stack. Spark SQL supports real-time data processing. ), 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 … The data is pulled into the memory in-parallel and in chunks. Also, can portion and bucket, tables in Apache Hive. We can use several programming languages in Spark SQL. On one side, Apache Pig relies on scripts and it requires special knowledge while Apache Hive is the answer for innate developers working on databases. I have done lot of research on Hive and Spark SQL. 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. This presentation was given at the Strata + Hadoop World, 2015 in San Jose. Apache Hive: To understand more, we will also focus on the usage area of both. Published on October 7, 2016 October 7, 2016 • 19 Likes • 0 Comments Yes, SparkSQL is much faster than Hive, especially if it performs only in-memory computations, but Impala is still faster than SparkSQL. Spark SQL supports only JDBC and ODBC. Hive can now be accessed and processed using spark SQL jobs. It supports several operating systems. Faster Execution - Spark SQL is faster than Hive. 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 uses Hadoop as its storage engine and only runs on HDFS. We will also cover the features of both individually. It is open sourced, through Apache Version 2. This allows data analytics frameworks to be written in any of these languages. Then, the resulting data sets are pushed across to their destination. Apache Hive: Building a Hadoop career is everyone’s dream in today’s IT industry. Hence, we can not say SparkSQL is not a replacement for Hive neither is the other way. Spark SQL connects hive using Hive Context and does not support any transactions. This makes Hive a cost-effective product that renders high performance and scalability. Users who are comfortable with SQL, Hive is mainly targeted towards them. Basically, it supports for making data persistent. Spark SQL: Spark SQL: In theory swapping out engines (MR, TEZ, Spark) should be easy. Because Spark performs analytics on data in-memory, it does not have to depend on disk space or use network bandwidth. Hive is an open-source distributed data warehousing database that operates on Hadoop Distributed File System. Spark SQL is a library whereas Hive is a framework. Spark applications can run up to 100x faster in terms of memory and 10x faster in terms of disk computational speed than Hadoop. A comparison of their capabilities will illustrate the various complex data processing problems these two products can address. Hive is originally developed by Facebook. Hive is slow but undoubtedly a great option for heavy ETL tasks where reliability plays a vital role, for instance the hourly log aggregations for advertising organizations.Impala is an open source SQL engine that can be used effectively for processing queries on huge volumes of data. Apache Hive: Spark claims to run 100 times faster than MapReduce. Let’s see few more difference between Apache Hive vs Spark SQL. Basically, we can implement Apache Hive on Java language. It makes Hive 2 practically 26x faster than Hive 1. * Created at AMPLabs in UC Berkeley as part of Berkeley Data Analytics Stack (BDAS). Spark SQL: Spark streaming is an extension of Spark that can stream live data in real-time from web sources to create various analytics. Spark SQL places first only for three queries (query 30, 41, and 81). 1) Explain the difference between Spark SQL and Hive. As same as Hive, Spark SQL also support for making data persistent. Note: LLAP is much more faster than any other execution engines. Indeed, Shark is compatible with Hive. Spark SQL is faster than Hive when it comes to processing speed. Afterwards, we will compare both on the basis of various features. You have learned that Spark SQL is like HIVE but faster. It’s faster because Impala is an engine designed especially for the mission of interactive SQL over HDFS, and it has architecture concepts that helps it achieve that. Spark however is faster than MapReduce which was the first compute engine created when HDFS was created. 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. I presume we can use Union type in Spark-SQL, Can you please confirm. Hive comes with enterprise-grade features and capabilities that can help organizations build efficient, high-end data warehousing solutions. Hive is a distributed database, and Spark is a framework for data analytics. Its SQL interface, HiveQL, makes it easier for developers who have RDBMS backgrounds to build and develop faster performing, scalable data warehousing type frameworks. However, Hive is planned as an interface or convenience for querying data stored in HDFS. Spark’s extension, Spark Streaming, can integrate smoothly with Kafka and Flume to build efficient and high-performing data pipelines. On the other hand, SQL being an old tool with powerful abilities is still an answer to our many needs. Apache Hive: Apache Hive: Apache Hive: [Hive-user] Hive on Spark VS Spark SQL; Guoqing0629. Apache Hive had certain limitations as mentioned below. Hadoop was already popular by then; shortly afterward, Hive, which was built on top of Hadoop, came along. Difference Between Apache Hive and Apache Spark SQL. There is a selectable replication factor for redundantly storing data on multiple nodes. See the original article here. Spark SQL: Spark uses lazy evaluation with the help of DAG (Directed Acyclic Graph) of consecutive transformations. Also, data analytics frameworks in Spark can be built using Java, Scala, Python, R, or even SQL. Like Apache Hive, it also possesses SQL-like DML and DDL statements. Hive and Spark are different products built for different purposes in the big data space. It really depends on the type of query you’re executing, environment and engine tuning parameters. Apache Spark works well for smaller data sets that can all fit into a server's RAM. The answer of question that why to choose Spark is that Spark SQL reuses Hive meta-store and frontend, that is fully compatible with existing Hive queries, data and UDFs. Before Spark came into the picture, these analytics were performed using MapReduce methodology. It achieves this high performance by performing intermediate operations in memory itself, thus reducing the number of read and writes operations on disk. As mentioned earlier, advanced data analytics often need to be performed on massive data sets. Apache Hive is the most popular and most widely used SQL solution for Hadoop. As a result, it can only process structured data read and written using SQL queries. Apache Hive is built on top of Hadoop. Also provides acceptable latency for interactive data browsing. With the massive amount of increase in big data technologies today, it is becoming very important to use the right tool for every process. But, using Hive, we just need to submit merely SQL queries. The data is stored in the form of tables (just like a RDBMS). Impala (“SQL on HDFS”) : Why Impala query speed is faster than Hive? In addition, Hive is not ideal for OLTP or OLAP operations. It has a Hive interface and uses HDFS to store the data across multiple servers for distributed data processing. For example C++, Java, PHP, and Python. It can run on thousands of nodes and can make use of commodity hardware. Given the fact that Berkeley invented Spark, however, these tests might not be completely unbiased. Spark SQL: To ke… Spark SQL, users can selectively use SQL constructs to write queries for Spark pipelines. However, Apache Pig works faster than Apache Hive. We will discuss all in detail to understand the difference between Hive and SparkSQL. Hive and Spark are two very popular and successful products for processing large-scale data sets. 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. Jdbc, ODBC, and Thrift than Hive, it is possible in several.. As Hive, it also has predefined data types be performed on massive data sets become Obsolete & get Pink! Spark works well when why spark sql is faster than hive with Kafka and Flume the DZone community and get the full member experience with as! To work on data in-memory, it also supports SQL-based data extraction than Hadoop MapReduce the DZone and. Similar as Hive, we will try to read data from existing Hive installation of each data extraction on data... Differences, Chef vs. Puppet: Methodologies, Concepts, and Thrift are different products built for different in. Into RDBMS databases can only scale vertically to processing speed this shows how Spark is potentially 100 times faster discuss. 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