Row vs Column Tables
Last updated
Last updated
Columnar storage is a key part of the data warehouse, but why is that? In this article we will review what columnar storage is and why it’s such a key part of the data warehouse.
In traditional Postgres, data in Postgres is stored in a heap table. Heap tables are arranged row-by-row, similar to how you would arrange data when creating a large spreadsheet. Data can be added forever by appending data to the end of the table.
In Postgres, heap tables are organized into pages. Each page holds 8kb of data. Each page holds pointers to the start of each row in the data.
Heap tables are optimized for transactional queries. Heap tables can use indexes to quickly find the row of data you are looking for — an index holds the page and row number for particular values of data. Generally, transactional workloads will read, insert, update, or delete small amounts of data at a time. Performance can scale so long as you have indexes to find the data you’re looking for.
Heap tables perform poorly when data cannot be found by an index, known as a table scan. In order to find the data, all data in the table must be read. Because the data is organized by row, you must read each row to find it. When you active dataset size grows beyond the available memory on the system, you will find these queries slow down tremendously.
Additionally, scans assisted by an index can only go so far if you are requesting a large amount of data. For example, if you would like to know the average over a given month and have an index on the timestamp, the index will help Postgres find the relevant data, but it will still need to read every target row individually in the table to compute the average.
Columnar tables are organized transversely from row tables. Instead of rows being added one after another, rows are inserted in bulk into a stripe. Within each stripe, data from each column is stored next to each other. Imagine rows of a table containing:
This data would be stored as follows in columnar:
In columnar, you can think of each stripe as a row of metadata that also holds up to 150,000 rows of data. Within each stripe, data is divided into chunks of 10,000 rows, and then within each chunk, there is a “row” for each column of data you stored in it. Additionally, columnar stores the minimum, maximum, and count for each column in each chunk.
Columnar is optimized for table scans — in fact, it doesn’t use indexes at all. Using columnar, it’s much quicker to obtain all data for a particular column. The database does not need to read data that you are not interested in. It also uses the metadata about the values in the chunks to eliminate reading data. This is a form of “auto-indexing” the data.
Because similar data is stored next to each other, very high data compression is possible. Data compression is an important benefit because columnar is often used to store huge amounts of data. By compressing the data, you can effectively read data more quickly from disk, which both reduces I/O and increasing effective fetch speed. It also has the effect of making better use of disk caching as data is cached in its compressed form. Lastly, you greatly reduce your storage costs.
Hydra Columnar storage is not designed to do common transactional queries like “find by ID” - the database will need to scan a much larger amount of data to fetch this information than a row table.
Hydra Columnar is append-only. While it supports updates and deletes (also known as Data Modification Language or DML), space is not reclaimed on deletion, and updates insert new data. Updates and deletes lock the table as Columnar does not have a row-level construct that can be locked. Overall, DML is considerably slower on columnar tables than row tables. Space can be later reclaimed using columnar.vacuum.
Lastly, columnar tables need to be inserted in bulk in order to create efficient stripes. This makes them ideal for long-term storage of data you already have, but not ideal when data is still streaming into the database. For these reasons, it’s best to store data in row-based tables until it is ready to be archived into columnar. You can compact small stripes by calling VACUUM.
Please read our operational documentation about using Hydra Columnar.
