Other major Presto users include Netflix (using Presto for analyzing more than 10 PB data stored in AWS S3), AirBnb and Dropbox. One example that illustrates the problem described above is Marek VavruÅ¡a’s post about Cloudflare’s choice between ClickHouse and Druid. These two don't belong to the same category and don't compete with each other same as Arrow doesn't compete with Hadoop. 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. Throttling functionality may limit the concurrent queries. 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 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. Speed: Presto is faster due to its optimized query engine and is best suited for interactive analysis. The actual implementation of Presto versus Drill for your use case is really an exercise left to you. Apache Arrow is an in-memory data structure specification for use by engineers building data systems. It doesn’t require schema definition which could lead to … It shares same features with Presto which makes it a good competitor. Comparison with Hive. RaptorX – Disaggregates the storage from compute for low latency to provide a unified, cheap, fast, and scalable solution to OLAP and interactive use cases. Apache Pinot and Druid Connectors – Docs. Design Docs. 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 Arrow is a proposed in-memory data layer designed to back different analytical loads. Apache Arrow is an open source technology Dremio helped create that also uses columnar data compression and many other optimizations that take advantage of in-memory computing and GPUs. Issue. 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 Does not need Hive metastore to query data on HDFS. It was mainly targeted for Data Science workloads to use a … Apache Arrow with Apache Spark. 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