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. AWS EKS/ECS and Fargate: Understanding the Differences, Chef vs. Puppet: Methodologies, Concepts, and Support, Developer It makes Hive 2 practically 26x faster than Hive 1. Hive is similar to an RDBMS database, but it is not a complete RDBMS. Spark SQL provides faster execution than Apache Hive. Hive helps perform large-scale data analysis for businesses on HDFS, making it a horizontally scalable database. Spark is a distributed big data framework that helps extract and process large volumes of data in RDD format for analytical purposes. Spark SQL: Applications needing to perform data extraction on huge data sets can employ Spark for faster analytics. Spark SQL: Apache Hive is the most popular and most widely used SQL solution for Hadoop. Basically, hive supports concurrent manipulation of data. Don't become Obsolete & get a Pink Slip One can achieve extra optimization in Apache Spark, with this extra information. Spark SQL supports only JDBC and ODBC. Hive is the standard SQL engine in Hadoop and one of the oldest. Indeed, Shark is compatible with Hive. 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. This reduces data shuffling and the execution is optimized. While Apache Hive and Spark SQL perform the same action, retrieving data, each does the task in a different way. At First, we have to write complex Map-Reduce jobs. It can run on thousands of nodes and can make use of commodity hardware. Hive was built for querying and analyzing big data. A comparison of their capabilities will illustrate the various complex data processing problems these two products can address. May 9, 2019. Apache Hive: * Created at AMPLabs in UC Berkeley as part of Berkeley Data Analytics Stack (BDAS). The core reason for choosing Hive is because it is a SQL interface operating on Hadoop. Primarily, its database model is also Relational DBMS. Hive is not an option for unstructured data. There are no access rights for users. Your email address will not be published. Hive comes with enterprise-grade features and capabilities that can help organizations build efficient, high-end data warehousing solutions. However, Apache Pig works faster than Apache Hive. HiveQL is a SQL engine that helps build complex SQL queries for data warehousing type operations. Although, no provision of error for oversize of varchar type. It is specially built for data warehousing operations and is not an option for OLTP or OLAP. It can also extract data from NoSQL databases like MongoDB. But, using Hive, we just need to submit merely SQL queries. In Apache Hive, latency for queries is generally very high. It possesses SQL-like DML and DDL statements. This blog totally aims at differences between Spark SQL vs Hive in Apache Spar… Also, data analytics frameworks in Spark can be built using Java, Scala, Python, R, or even 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. Spark SQL vs. Hive QL- Advantages of Spark SQL over HiveQL. 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.) Spark SQL supports real-time data processing. It is an RDBMS-like database, but is not 100% RDBMS. Apache Hive: Spark SQL is faster than Hive when it comes to processing speed. Given the fact that Berkeley invented Spark, however, these tests might not be completely unbiased. Spark SQL: As JDBC/ODBC drivers are available in Hive, we can use it. However, Hive is planned as an interface or convenience for querying data stored in HDFS. This makes Hive a cost-effective product that renders high performance and scalability. Apache Hive: Spark operates quickly because it performs complex analytics in-memory. Hive can be integrated with other distributed databases like HBase and with NoSQL databases, such as Cassandra. Hence, if you’re already familiar with SQL but not a programmer, this blog might have shown you … As a result, we have seen that SparkSQL is more spark API and developer friendly. This time, instead of reading from a file, we will try to read from a Hive SQL table. Hive brings in SQL capability on top of Hadoop, making it a horizontally scalable database and a great choice for DWH environments. It has predefined data types. So, hopefully, this blog may answer all the questions occurred in mind regarding Apache Hive vs Spark SQL. It provides a faster, more modern alternative to MapReduce. Apache Hive is built on top of Hadoop. See the original article here. Basically, we can implement Apache Hive on Java language. 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 is 100 times faster than MapReduce and this shows how Spark is better than Hadoop MapReduce. 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. It achieves this high performance by performing intermediate operations in memory itself, thus reducing the number of read and writes operations on disk. Apache Hive: 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. Published at DZone with permission of Daniel Berman, DZone MVB. Spark SQL:   For example Linux OS, X,  and Windows. Apache Hive was first released in 2012. They needed a database that could scale horizontally and handle really large volumes of data. Spark SQL: Before comparison, we will also discuss the introduction of both these technologies. You have learned that Spark SQL is like HIVE but faster. 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. Whereas, spark SQL also supports concurrent manipulation of data. Note: LLAP is much more faster than any other execution engines. Though, MySQL is planned for online operations requiring many reads and writes. So, when Hadoop was created, there were only two things. Moreover, It is an open source data warehouse system. Spark SQL: Apache Hive: For Example, float or date. In theory swapping out engines (MR, TEZ, Spark) should be easy. It uses spark core for storing data on different nodes. Hive uses Hadoop as its storage engine and only runs on HDFS. Spark was introduced as an alternative to MapReduce, a slow and resource-intensive programming model. Spark SQL:   Spark’s extension, Spark Streaming, can integrate smoothly with Kafka and Flume to build efficient and high-performing data pipelines. Spark SQL: Spark SQL: Spark has an answer to Hive called Shark that allows you to run SQL queries on Spark data. Its SQL interface, HiveQL, makes it easier for developers who have RDBMS backgrounds to build and develop faster performing, scalable data warehousing type frameworks. Apache Hive’s logo. To understand more, we will also focus on the usage area of both. Apache Hive: Spark supports different programming languages like Java, Python, and Scala that are immensely popular in big data and data analytics spaces. And Spark RDD now is just an internal implementation of it. Building a Hadoop career is everyone’s dream in today’s IT industry. Published on ... Two Fundamental Changes in Apache Spark. We will discuss all in detail to understand the difference between Hive and SparkSQL. Hive is a distributed database, and Spark is a framework for data analytics. Hadoop is more cost effective processing massive data sets. Hive Architecture is quite simple. Hive and Spark are both immensely popular tools in the big data world. To ke… Although, Interaction with Spark SQL is possible in several ways. Overall the user should find Hive-LLAP and Hive on MR3 running much faster than Spark SQL for typical queries. Apache Hive: While Apache Hive and Spark SQL perform the same action, retrieving data, each does the task in a different way. Spark claims to run 100 times faster than MapReduce. 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. At the time, Facebook loaded their data into RDBMS databases using Python. It has a Hive interface and uses HDFS to store the data across multiple servers for distributed data processing. However, Hive is planned as an interface or convenience for querying data stored in HDFS. Key-value store Your email address will not be published. Also, helps for analyzing and querying large datasets stored in Hadoop files. It really depends on the type of query you’re executing, environment and engine tuning parameters. Spark SQL connects hive using Hive Context and does not support any transactions. Also, there are several limitations with Hive as well as SQL. Explore Apache Hive Career to become a Hadoop Professional. Spark SQL: In addition, Hive is not ideal for OLTP or OLAP operations. Spark SQL: Data operations can be performed using a SQL interface called HiveQL. 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. Why Spark? Join the DZone community and get the full member experience. As mentioned earlier, advanced data analytics often need to be performed on massive data sets. Apache Hive: Note: ANSI SQL-92 is the third revision of the SQL database query language. But later donated to the Apache Software Foundation, which has maintained it since. Because of its ability to perform advanced analytics, Spark stands out when compared to other data streaming tools like Kafka and Flume. Spark SQL: Typically, Spark architecture includes Spark Streaming, Spark SQL, a machine learning library, graph processing, a Spark core engine, and data stores like HDFS, MongoDB, and Cassandra. Hadoop was already popular by then; shortly afterward, Hive, which was built on top of Hadoop, came along. Apache Spark works well for smaller data sets that can all fit into a server's RAM. This capability reduces Disk I/O and network contention, making it ten times or even a hundred times faster. Apache Hive: Conclusion. Hive on Spark provides us right away all the tremendous benefits of Hive and Spark both. Furthermore, Apache Hive has better access choices and features than that in Apache Pig. 1) Explain the difference between Spark SQL and Hive. Apache Spark is potentially 100 times faster than Hadoop MapReduce. Here is a quick summary of this video. Over a million developers have joined DZone. So we will discuss Apache Hive vs Spark SQL on the basis of their feature. We can use several programming languages in Spark SQL. Basically, for redundantly storing data on multiple nodes, there is a no replication factor in Spark SQL. In Spark, we use Spark SQL for structured data processing. Spark has its own SQL engine and works well when integrated with Kafka and Flume. This article focuses on describing the history and various features of both products. Impala is faster and handles bigger volumes of data than Hive query engine. Spark SQL: While Apache Spark SQL was first released in 2014. Hive is basically a front ... Why Is Impala Faster Than Hive? 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). Hence, we can not say SparkSQL is not a replacement for Hive neither is the other way. These tools have limited support for SQL and can help applications perform analytics and report on larger data sets. Though SQL-like query engines on non-SQL data stores is not a new concept (c.f., Hive, Shark, etc. Let’s see few more difference between Apache Hive vs Spark SQL. In general, it is hard to say if Presto is definitely faster or slower than Spark SQL. Apache Hive supports JDBC, ODBC, and Thrift. Spark SQL places first only for three queries (query 30, 41, and 81). Moreover, We get more information of the structure of data by using SQL. In other words, they do big data analytics. Currently released on 24 October 2017:  version 2.3.1 Primarily, its database model is Relational DBMS. It supports several operating systems. 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. Hadoop is a distributed file system (HDFS) while Spark is a compute engine running on top of Hadoop or your local file system. 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. Also discussed complete discussion of Apache Hive vs Spark SQL. 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 pull data from any data store running on Hadoop and perform complex analytics in-memory and in-parallel. It is originally developed by Apache Software Foundation. Hive is originally developed by Facebook. Apache Hive:   Hive does not support online transaction processing. Spark SQL: Also, can portion and bucket, tables in Apache Hive. I have done lot of research on Hive and Spark SQL. Hive is a pure data warehousing database that stores data in the form of tables. Apache Hive: It is open sourced, through Apache Version 2. Apache 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. Follow DataFlair on Google News & Stay ahead of the game. And all top level libraries are being re-written to work on data frames. Apache Hive: We will also cover the features of both individually. Apache Spark utilizes RAM and isn’t tied to Hadoop’s two-stage paradigm. This presentation was given at the Strata + Hadoop World, 2015 in San Jose. Yes, SparkSQL is much faster than Hive, especially if it performs only in-memory computations, but Impala is still faster than SparkSQL. We can use several programming languages in Hive. It does not offer real-time queries and row level updates. While, Hive’s ability to switch execution engines, is efficient to query huge data sets. 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