- 1 Does Presto require Hive?
- 2 Is Presto and Hive same?
- 3 Does Presto use Hive Metastore?
- 4 What is Hive Presto?
- 5 How does Presto connect to Hive?
- 6 What is the difference between MapReduce and Hadoop?
- 7 Can Presto query S3?
- 8 How does Presto work with Hive?
- 9 How do I create an external table in Presto?
- 10 Does MapReduce run Spark?
- 11 Is Spark better than MapReduce?
- 12 What is the difference between Spark and presto?
- 13 Is Presto better than Spark?
Does Presto require Hive?
Hive is optimized for query throughput, while Presto is optimized for latency. Presto has a limitation on the maximum amount of memory that each task in a query can store, so if a query requires a large amount of memory, the query simply fails….How to Best Use Hive and Presto.
•Aug 17, 2019
Is Presto and Hive same?
Presto vs Hive Presto is designed for low latency while on the other hand Hive is used for query throughput and queries that require very large amount of memory. We can also use both tools to explore data sitting on top of a Hadoop system.
Does Presto use Hive Metastore?
Presto is the SQL Engine to plan and execute queries, S3 is the storage service for table partition files, and Hive Metastore is the catalog service for Presto to access table schema and location information.
What is Hive Presto?
Apache Hive and Presto are both analytics engines that businesses can use to generate insights and enable data analytics. Apache Hive is a data warehousing tool designed to easily output analytics results to Hadoop. In contrast, Presto is built to process SQL queries of any size at high speeds.
How does Presto connect to Hive?
You can start Presto CLI to connect Hive storage plugin using the following command. $ ./presto –server localhost:8080 –catalog hive —schema tutorials; You will receive the following response.
What is the difference between MapReduce and Hadoop?
The Apache Hadoop is an eco-system which provides an environment which is reliable, scalable and ready for distributed computing. MapReduce is a submodule of this project which is a programming model and is used to process huge datasets which sits on HDFS (Hadoop distributed file system).
Can Presto query S3?
Presto contains several built-in connectors, the Hive connector is used to query data on HDFS or on S3-compatible engines. The Hive connector doesn't need Hive to parse or execute the SQL query in any way.
How does Presto work with Hive?
Presto is designed to comply with ANSI SQL, while Hive uses HiveQL. Presto can handle limited amounts of data, so it's better to use Hive when generating large reports. … Hive uses map-reduce architecture and writes data to disk while Presto uses HDFS architecture without map-reduce.
How do I create an external table in Presto?
Presto does not support creating external tables in Hive (both HDFS and S3). If you want to create a table in Hive with data in S3, you have to do it from Hive. Also, CREATE TABLE.. AS query, where query is a SELECT query on the S3 table will not create the table on S3.
Does MapReduce run Spark?
Originally developed at UC Berkeley's AMPLab, Spark was first released as an open-source project in 2010. Spark uses the Hadoop MapReduce distributed computing framework as its foundation. … Spark includes a core data processing engine, as well as libraries for SQL, machine learning, and stream processing.
Is Spark better than MapReduce?
The primary difference between Spark and MapReduce is that Spark processes and retains data in memory for subsequent steps, whereas MapReduce processes data on disk. As a result, for smaller workloads, Spark's data processing speeds are up to 100x faster than MapReduce.
What is the difference between Spark and presto?
Spark is more general in its applications, often used for data transformation and Machine Learning workloads. Presto supports querying data in object stores like S3 by default, and has many connectors available. It also works really well with Parquet and Orc format data.
Is Presto better than Spark?
Presto queries can generally run faster than Spark queries because Presto has no built-in fault-tolerance. Spark does support fault-tolerance and can recover data if there's a failure in the process, but actively planning for failure creates overhead that impacts Spark's query performance.