One example that illustrates the problem described above is Marek VavruÅ¡a’s post about Cloudflare’s choice between ClickHouse and Druid. It doesn’t require schema definition which could lead to … The actual implementation of Presto versus Drill for your use case is really an exercise left to you. It uses Apache Arrow for In-memory computations. CloudFlare: ClickHouse vs. Druid. Apache Spark is a storage agnostic cluster computing framework. Disaggregated Coordinator (a.k.a. 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. They needed 4 ClickHouse servers (than scaled to 9), and estimated that similar Druid deployment would need “hundreds of nodes”. 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 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. 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. Presto allows for data queries that traverse data stores and locations - a big plus in the multi-everything world of big data analytics. Hive, in comparison is slower. Design Docs. It shares same features with Presto which makes it a good competitor. In this post, I will share the difference in design goals. Apache Arrow with Apache Spark. RaptorX – Disaggregates the storage from compute for low latency to provide a unified, cheap, fast, and scalable solution to OLAP and interactive use cases. It was mainly targeted for Data Science workloads to use a … 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. Issue. Comparison with Hive. Apache Pinot and Druid Connectors – Docs. Throttling functionality may limit the concurrent queries. Presto-on-Spark Runs Presto code as a library within Spark executor. 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