![]() ![]() You can also modify the table attribute:.You can specify the table attribute when you create a table.ĬREATE TABLE student (id INT, name STRING).This feature is controlled by the table attribute : This avoids the generation of small files. For each partition, a special executor is used to merge writes to the partition by other e xecutors. Databricks optimizes the process of writes of Delta tables. In the end, many small files are generated in one partition. When each executor writes data to a partition in open-source Spark, it creates a table file for writing. Feature 1: Optimize the Writes of Delta Tables to Avoid the Generation of Small Files On the other hand, it increases the metadata of Delta tables and slows down the acquisition of metadata, which reduces the reading efficiency of the table from another dimension.ĭatabricks provides three optimization features from two dimensions to solve the problem of small files in Delta Lake, avoiding the generation of small files and automatically (or manually) merging small files. On the one hand, the increase in the number of small files reduces the amount of data that Spark reads serially each time, which reduces the reading efficiency. If you frequently perform merge, update, and insert operations in Delta Lake or insert data into Delta tables in scenarios of stream processing, a large number of small files will be generated in Delta tables. Optimized Solutions for the Problem of Small Files ![]() This article describes how to optimize the performance of the product features provided by the Enterprise Edition to help you efficiently access lake houses. In addition to the open-source Delta Lake OSS led by the community, the commercial products of Databricks provide the Spark and Delta Lake engine of the Enterprise Edition. It officially became open-source in 2019. DDI provides users with services in data analysis, data engineering, data science, and AI to build an integrated lake house architecture.ĭelta Lake is a transaction-enabled data lake product developed by Databricks since 2016. In 2020, Databricks and Alibaba Cloud jointly built a full hosting big data analysis and AI platform based on Apache Spark called Databricks DataInsight (DDI). It focuses on open-source ecosystems, such as Spark, Delta Lake, and MLFlow, to build enterprise-class lake house products. It is the founding company of Apache Spark and the largest code contributor to Spark. By Li Jingui (Jinxi), Development Engineer of Open-Source Big Data Platform of Alibaba Cloud, and Wang Xiaolong (Xiaolong), Technical Expert of Open-Source Big Data Platform of Alibaba Cloud Backgroundĭatabricks is a leading enterprise specializing in data and artificial intelligence (AI). ![]()
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