Overall Hive 3.0.0 on MR3 is comparable to Hive-LLAP:
We often ask questions on the performance of SQL-on-Hadoop systems:While interesting in their own right, these questions are particularly relevant to industrial practitioners who want to adopt the most appropriate technology to meet their need.There are a plethora of benchmark results available on the internet, but we still need new benchmark results.
a system may not be configured at all to achieve the best performance. Previous. Presto supported syntax for 9 of 10 queries, with queries running between 18.89 and 506.84 seconds. hive.parquet-optimized-reader.enabled=true hive.parquet-predicate-pushdown.enabled=true Benchmark result: I don’t know why presto sucks when perform join … Next. A Presto resource group is an admission control and workload management mechanism that manages resource allocation.
Configuration Settings
Specifically, it allows any number of files per bucket, including zero. There is much discussion in the industry about analytic engines and, specifically, which engines best meet various analytic needs. we attach two tables containing the raw data of the experiment. This post looks at two popular engines, Hive and Presto, and assesses the best uses for each.Hive translates SQL queries into multiple stages of MapReduce and it is powerful enough to handle huge numbers of jobs (Although as Arun C Murthy In some instances simply processing SQL queries is not enough—it is necessary to process queries as quickly as possible so that data scientists and analysts can use Treasure Data for quickly gaining insights from their data collections. Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes.
Since all SQL-on-Hadoop systems constantly evolve, the landscape gradually changes and previous benchmark results may already be obsolete.
Kiyoto Tamura leads marketing at Treasure Data and is a maintainer of Fluentd , the open source data collector to unify log management. This allows inserting data into an existing partition without having to rewrite the entire partition, and improves the performance of writes by not requiring the creation of files for empty buckets. We have hundreds of petabytes of data and tens of thousands of Apache Hive tables.
The results are by no means definitive, but should shed light on where each system lies and in which direction it is moving in the dynamic landscape of SQL-on-Hadoop. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Then, start hive metastore using the following command, hive --service metastore Presto uses Hive metastore service to get the hive table’s details. Hive; Hopefully you have installed Hadoop and Hive on your machine.
Wikitechy Apache Hive tutorials provides you the base of all the following topics . and a negative running time, e.g., -639.367, means that the query fails in 639.367 seconds. An LLAP daemon uses 160GB on the Red cluster and 76GB on the Gold cluster.
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. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Such error handling logic (or a lack thereof) is acceptable for interactive queries; however, for daily/weekly reports that must run reliably, it is ill-suited. Hive 0.12 supported syntax for 7/10 queries, with … Here is a link to [We count the number of queries that successfully return answers:We measure the total running time of all queries, whether successful or not:Unfortunately it is hard to make a fair comparison from this result because not all the systems are consistent in the set of completed queries.
It is a reactive gating mechanism that checks whether a resource group has exceeded its limit before letting it start a new query. by virtue of its comparable speed and such additional features as elastic allocation of cluster resources, full implementation of impersonation, easy deployment, and so on. Features that can be implemented on top of PyHive, such integration with your favorite data analysis library, are likely out of scope.