Apache Hive, Apache Spark, and Presto are all popular open-source tools for working with data lakes and data lakehouses. However, these tools typically serve different functions – and while some of these overlap, there are also many differences, typically making them complimentary rather than competitive. Let’s look at the Presto vs Hive vs Spark, and see how each of these tools can be used for large-scale data analysis.
What is Apache Hive?
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data analysis and query. Hive provides an SQL-like interface called HiveQL to query large dataset stored in Hadoop’s HDFS and compatible file systems such as Amazon S3.
Presto is a high-performance, distributed SQL query engine for big data. Its architecture allows users to query a variety of data sources such as Hadoop, AWS S3, MySQL, and other relational and non-relational databases. One can even query data from multiple data sources within a single query.
What is Apache Spark?
Apache Spark is a unified analytics engine for large-scale data processing. Spark provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.
It can run in Hadoop clusters through YARN or Spark’s standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop Input Format. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Presto vs Hive vs Spark: The Comparison
- All three projects – Presto, Hive, and Spark – are community-driven open-source software, with the latter two released under the Apache License.
- They are distributed “Big Data” software frameworks
- BI tools connect to them using JDBC/ODBC
- They provide query capabilities on top of Hadoop and AWS S3
- They have been tested and deployed at petabyte-scale companies
- They can be run on-prem or in the cloud.
|Function||MPP SQL engine||MPP SQL engine||General purpose execution framework|
|Processing Type||Batch processing using Apache Tez or MapReduce compute frameworks||Executes queries in memory, pipelined across the network between stages, thus avoiding unnecessary I/O||Optimized directed acyclic graph (DAG) execution engine and actively caches data in-memory|
|SQL Support||HiveQL||ANSI SQL||Spark SQL|
|Usage||Optimized for query throughput||Optimized for latency||General purpose, often used for data transformation and Machine Learning workloads|
|Use cases||Large data aggregations||Interactive queries and quick data exploration.||General purpose, often used for data transformation and Machine Learning workloads.|
Hive vs Presto
Both Presto and Hive are used to query data in distributed storage, but Presto is more focused on analytical querying whereas Hive is mostly used to facilitate data access. Hive provides a virtual data warehouse that imposes structure on semi-structured datasets, which can then be queried using Spark, MapReduce, or Presto itself. Presto is a compute and querying layer that can connect to the Hive Metastore or other data catalogs such as Apache Iceberg.
|Common use case:||Query data stored in distributed storage|
|Hive:||Facilitates data access|
|Presto:||Focused on analytical querying|
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