SQL on Hadoop with Hive makes Big Data accessible. Yet performance can lack. RCFile (Record Columnar File) are great optimisation for Big Data with Hive.
The previous two posts in this four parts series explained the reasons why to use text on the periphery of an ETL process and optimisations for text. The inside of a Hive data processing pipeline can be optimised further for performance though. Commonly, derived, intermediate tables are queried heavily. Optimising these tables speeds up the whole pipeline greatly. How can these potentially huge tables be query agnostically optimised to only read and process the relevant fraction of the data stored?
Continuing the scenario from the previous posts: a startup ingests and transforms with Hive logs from (HDFS) filesystem containing user interactions, transactional and user data from a SQL, and factual product information from a NoSQL store. The business questions arising now are around slicing and dicing the data on demand, e.g. which region sold what when, what kind of user interactions lead to sales, and so forth. Increasingly, queries become ad-hoc and demand answers within a few minutes and not potentially hours. Additionally, the data is growing and the scheduled nightly batch processing for reports and search index takes longer every day.
At the core of the ETL process, Hive parses log information and aggregates it in a dedicated table. Combinations of the log information with the user, transaction, and product information then lead to sizeable derived tables. Some of the data is planned to feed into machine learning tools for predictive analysis, clustering, and classification. Consequently, a lot of features are recorded in additional columns. The resulting tables have become numerous and large, and are central for subsequent reports and data products. Only a fraction or aggregation of the data is exported to external services and tools. Therefore text for the time being is feasible for the import and export. Internally, optimisation is needed to satisfy ad-hoc queries and maintain execution time and cost.
In scenario a central table pulls together all the data and has billions of rows with product user interactions with dozens of columns. Ideally, a specific report or a one-off query by a business stakeholder translates into reading and processing only the smallest amount of data. That requires Hive to skip the maximum amount of rows and columns whenever possible.
One way to break down the tables is to partition them and save different data in different subdirectories, e.g. split it into years, months, and days, or regions. Nevertheless, partitioning is only sensible for large predictable splits. Hive would still have to read and parse terabytes or at least gigabytes to find as little as a single value in specific column of a specific row. More importantly, doing so would waste resources and time answering the question.
In traditional DBMS, creating dedicated indices of the data solves these problems. Recent research has demonstrated that indices are attainable for Hadoop and bring tremendous performance benefits. There are more options available today that can further improve the performance.
Traditionally, Hadoop saves its data internally in flat sequence files, which is a binary storage format for key value pairs. It has the benefit of being more compact than text and fits well the map-reduce output format. Sequence files can be compressed on value, or block level, to improve its IO profile further. Unfortunately, sequence files are not an optimal solution for Hive since it saves a complete row as a single binary value. Consequently, Hive has to read a full row and decompress it even if only one column is being requested.
The state-of-the-art solution for Hive is the RCFile. The format has been co-developed by Facebook, which is running the largest Hadoop and Hive installation in the world. RCFile has been adopted by the Hive and Pig projects as the core format for table like data storage. The goal of the format development was “(1) fast data loading, (2) fast query processing, (3) highly efficient storage space utilization, and (4) strong adaptivity to highly dynamic workload patterns,” as can be seen in this PDF from the development teams.
The RCFile splits data horizontally into row groups. For example, rows 1 to 100 are stored in one group and rows 101 to 200 in the next and so on. One or several groups are stored in a HDFS file. The RCFile saves the row group data in a columnar format. So instead of storing row one then row two, it stores column one across all rows then column two across all rows and so on.
The benefit of this data organization is that Hadoop’s parallelism still applies since the row groups in different files are distributed redundantly across the cluster and processable at the same time. Subsequently, each processing node reads only the columns relevant to a query from a file and skips irrelevant ones. Additionally, compression on a column base is more efficient. It can take advantage of similarity of the data in a column.
Storing the intermediate tables as (compressed) RCFile reduces the IO and storage requirements significantly over text, sequence file, and row formats in general. Querying tables stored in RCFile format allows Hive to skip large parts of the data and get the results faster and cheaper. An experiment, from the original RCFile paper, with Facebook data with a 1.3 TB table with 38 columns containing ad-click data, demonstrates that RCFile tables are smaller and can be more than three times faster in processing.
The last part of this series will review the most recent evolution of this storage format, the ORC (Optimized Row Columnar) file format. ORC provides exciting, substantial improvements beyond RCFile.