Issue. Speed: Presto is faster due to its optimized query engine and is best suited for interactive analysis. They needed 4 ClickHouse servers (than scaled to 9), and estimated that similar Druid deployment would need “hundreds of nodes”. Hive, in comparison is slower. The actual implementation of Presto versus Drill for your use case is really an exercise left to you. Presto allows for data queries that traverse data stores and locations - a big plus in the multi-everything world of big data analytics. Apache Arrow is integrated with Spark since version 2.3, exists good presentations about optimizing times avoiding serialization & deserialization process and integrating with other libraries like a presentation about accelerating Tensorflow Apache Arrow on Spark from Holden Karau. Apache Pinot and Druid Connectors – Docs. It doesn’t require schema definition which could lead to … It uses Apache Arrow for In-memory computations. is it possible to query in memory arrow table using presto or is there some way to use a pandas data frame as a data source for presto query engine Ask Question Asked 2 years, 9 months ago Throttling functionality may limit the concurrent queries. CloudFlare: ClickHouse vs. Druid. Other major Presto users include Netflix (using Presto for analyzing more than 10 PB data stored in AWS S3), AirBnb and Dropbox. RaptorX – Disaggregates the storage from compute for low latency to provide a unified, cheap, fast, and scalable solution to OLAP and interactive use cases. Does not need Hive metastore to query data on HDFS. Apache Arrow with Apache Spark. Design Docs. Disaggregated Coordinator (a.k.a. Presto-on-Spark Runs Presto code as a library within Spark executor. Apache Spark is a storage agnostic cluster computing framework. Apache Arrow is a proposed in-memory data layer designed to back different analytical loads. This post is focused on the performance of Presto, more specifically on the performance comparison between Amazon’s S3 object storage service and MinIO’s object storage software. Apache Arrow is an in-memory data structure specification for use by engineers building data systems. It shares same features with Presto which makes it a good competitor. The original reader conducts analysis in three steps: (1) reads all Parquet data row by row using the open source Parquet library; (2) transforms row-based Parquet records into columnar Presto blocks in-memory for all nested columns; and (3) evaluates the predicate (base.city_id=12) on these blocks, executing the queries in our Presto engine. These two don't belong to the same category and don't compete with each other same as Arrow doesn't compete with Hadoop. 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