![]() ![]() In a row storage format, each record in the dataset has to be loaded, parsed into fields, and extracted the data for Name. It will read only the required columns since they are adjacent, thus minimizing IO.įor example, let’s say you want only the NAME column. The columnar storage format is more efficient when you need to query a few columns from a table. If we take the same record schema as mentioned above, having three fields ID (int), NAME (varchar), and Department (varchar), the table will look something like this: In a column-oriented format, the values of each column of the same type in the records are stored together.įor example For example, if there is a record comprising ID, employee Name, and Department, then all the values for the ID column will be stored together, values for the Name column together, and so on. In order to understand the Parquet file format in Hadoop better, first, let’s see what a columnar format is. It is especially good for queries that read particular columns from a “wide” (with many columns) table since only needed columns are read, and IO is minimized. Ĭompared to a traditional approach where data is stored in a row-oriented approach, Parquet file format is more efficient in terms of storage and performance. Parquet, an open-source file format for Hadoop, stores nested data structures in a flat columnar format. Some file formats are designed for general use, others are designed for more specific use cases, and some are designed with specific data characteristics in mind. ![]() The various Hadoop file formats have evolved in data engineering solutions to ease these issues across a number of use cases.Ĭhoosing an appropriate file format can have some significant benefits: ![]() As the data increases, the cost for processing and storage increases too. Along with the storage cost, processing the data comes with CPU, Network, IO costs, etc. When processing Big data, the cost required to store such data is more (Hadoop stores data redundantly to achieve fault tolerance). These issues get complicated with the difficulties of managing large datasets, such as evolving schemas or storage constraints. Why do we need different file formats?Ī huge bottleneck for HDFS-enabled applications like MapReduce and Spark is the time it takes to find relevant data in a particular location and the time it takes to write the data back to another location. You can also read a few other interesting case studies on how different big data file formats can be handled using Hadoop managed services here. In this blog, I will discuss what file formats are, go through some common Hadoop file format features, and give a little advice on which format you should be using. What are file formats? What are the common Hadoop file format features? Which format should you be using? ![]()
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