In my previous article I have explained about the Hive interview questions and about Hive architecture in detail. I would like to throw light on Hive vs Spark which are most important and most used technologies for handing data. I would like to explain about highlights of Hive vs Spark with real life examples. Two very successful and well-liked tools for handling massive data sets are Hive and Spark. They perform big data analytics, in other words. The history and many features of both products are mostly discussed in this page. The different complicated data processing issues these two solutions can solve will be demonstrated by a comparison of their capabilities.
On the Hadoop Distributed File System, Hive is an open-source distributed data warehousing database. For searching and analyzing massive data, Hive was created. Tables are used to hold the data (just like a RDBMS). A SQL interface called hiveql can be used to conduct data operations. Hadoop becomes a horizontally scalable database when Hive adds SQL support, making it a fantastic option for DWH setups. Hive Architecture vs spark architecture.
Hive vs Spark – Step By Step : Architecture differences :
Hive : Hive architecture is really straightforward. It uses HDFS to store the data across numerous servers for distributed data processing and provides a Hive interface.
Spark : Depending on the needs, the Spark architecture can change. The typical Spark architecture consists of data storage like HDFS, MongoDB, and Cassandra, as well as Spark Streaming, Spark SQL, a machine learning library, graph processing, and a Spark core engine.
Hive vs Spark : Performance Differences :
Although the MapReduce algorithm in Hadoop is good, Spark employs MapReduce more effectively, which allows for processing to happen more quickly. When using MapReduce to process data, Hadoop frequently accesses the disc, which can cause a slower job run.
Spark Performance :
The fact that Spark relies on data being saved in memory rather than frequent disc access is another performance differentiator for Spark. As a result, the need for greater memory makes Spark more expensive. Hadoop can be used with less expensive commodity hardware.
Hive vs Spark : Usages Difference :
- Flexibility and evolution of the schema.
- Additionally, Apache Hive tables can divide and bucket data.
- Hive allows us to use JDBC/ODBC drivers, therefore we can use it
1.In essence, it executes SQL queries
2.It is feasible to read data from an existing Hive installation using Spark SQL.
3.If we use another programming language to run Spark SQL, the outcome is returned as a Dataset or DataFrame.
As opposed to Spark, which makes use of Resilient Distributed Datasets (RDDs), Hadoop requires developers to design the code that will process the data in batches. Hadoop needs new capabilities for stream processing and machine learning, which Spark currently has.
With its low-level APIs, Hadoop can be relatively difficult to use, whereas Spark encapsulates these complexities using high-level operators. Because all data is computed in memory with Spark, there is no need for an external task scheduler like there is with Hadoop.
Hive vs Spark : Features and Capabilities differences :
Hive has features and capabilities that are suitable for the enterprise, enabling businesses to create effective, cutting-edge data warehousing solutions. Some of these features include:
● Hive’s features and capabilities are appropriate for the enterprise, enabling companies to develop cutting-edge data warehousing solutions that are efficient and effective.
● A SQL engine called HiveQL facilitates the creation of sophisticated SQL queries for tasks resembling data warehousing. Other distributed databases like HBase and NoSQL databases like Cassandra can be combined with Hive.
Spark Features and Capabilities :
● Hadoop data is extracted by Spark, which then runs analytics in-memory. In parallel and in chunks, the data is read into memory. The resultant data sets are then transferred to their final location. The data sets may also remain in memory up to consumption. Streaming with Spark.
● Large amounts of data from popular web sources can be streamed live with the Spark Streaming addon. When compared to other data streaming platforms like Kafka and Flume, Spark stands out due to its capacity for doing advanced analyses.
Hive vs Spark : Difference in Tabular Format
While Hive’s default execution engine is MapReduce, Spark SQL’s execution engine is Spark Core.
● Spark SQL is dependent on Hive’s metadata.
● The majority of Hive’s syntax and functions are compatible with Spark SQL.
● The unique Hive functions can be used by Spark SQL.
● Spark SQL executes queries 10 to 100 times quicker than Hive.
● Buckets are not supported by Spark SQL, although they are by Hive.
I hope you like this article on Hive vs Spark. If you like this article or if you have any issues with the same kindly comment in comments section.