Google SWE teaches systems design | EP28: Time Series Databases

Summary

This video explores the design and benefits of time series databases, tailored for storing data that streams in over time, like logs or sensor readings. It discusses the importance of sorted data, compound indexing, and column-oriented storage for efficient read and write operations. The script delves into unique features like hyper tables and chunking, which optimize performance, caching, and deletion of old data. The presenter emphasizes that while not every application requires time series databases, they are highly efficient for handling such data, making them a valuable tool in the right scenarios.

Takeaways

• 🕒 Time Series Databases are specialized for storing time-stamped data, such as logs from servers or sensor data.
• 🔍 They are designed for high read and write throughput, particularly for ordered time series data.
• 📈 Write operations typically involve recent time intervals and are not usually updated once written.
• 🔗 Adjacent values in time series data are often similar, which can be leveraged for efficient data storage.
• 🗂️ A compound index using timestamp and data source ID can effectively manage data from multiple sources over time intervals.
• 📊 Reads are often focused on a single column of data and from a relatively small time interval, such as hours, days, or weeks.
• 🗑️ Deletion of time series data often involves removing old data that is no longer relevant, such as data older than six months.
• 🔑 Sorting data by timestamp and source ID creates a natural compound index, which is beneficial for performance.
• 📚 Column-oriented storage is advantageous for time series data as it simplifies aggregations and reduces disk I/O.
• 🔬 Compression techniques can significantly reduce the storage space needed for time series data due to the similarity of values.
• 🧩 The 'hyper table' concept, which is a collection of smaller 'chunk tables', optimizes performance by caching only relevant indexes in memory.
• 🚀 This chunking design also simplifies the deletion process, allowing for the quick removal of entire chunks of outdated data.

Q & A

What are time series databases and why are they used?

Time series databases are specialized databases designed to handle time-stamped data, such as logs from servers or sensor data. They are used because they are optimized for high read and write throughput for ordered time series data, which makes them efficient for storing and querying large volumes of data that are collected over time.

What kind of data is typically stored in time series databases?

Time series databases are used to store data that comes in a consistent stream over a time interval, such as logs from servers, sensor readings, or any data that changes over time and requires time-stamping.

Why are time series databases optimized for writes to a recent time interval?

Writes are optimized for recent time intervals because time series data, such as sensor readings, are typically recorded once and not updated. They are usually appended towards the end of a time interval, making it efficient to write new data to the most recent part of the database.

How are time series databases optimized for reading data?

Time series databases are optimized for reading data by using column-oriented storage, which is efficient for aggregations and reduces disk I/O. Additionally, they use a 'hyper table' concept where data is divided into smaller chunks, allowing for faster access by keeping only relevant chunks' indexes in memory.

What is a 'hyper table' in the context of time series databases?

A 'hyper table' is a concept where all time series data on a node is represented as one huge table. However, this table is actually composed of many smaller 'chunk tables', each representing a combination of a source and a time interval. This design allows for efficient caching and deletion of data.

How do time series databases handle data that is no longer relevant, such as older time series data?

Time series databases often handle irrelevant data by deleting it. A common practice is to set a time threshold, like six months, and delete data older than this threshold, as it is no longer needed for analysis or other purposes.

What is the benefit of using a compound index with timestamp and data source ID in time series databases?

Using a compound index with timestamp and data source ID allows for efficient querying and organization of data from multiple sources over various time intervals. It ensures that data from the same source is grouped together and ordered by timestamp, which is beneficial for both write and read operations.

Why is column-oriented storage beneficial for time series databases?

Column-oriented storage is beneficial for time series databases because it simplifies the process of reading and aggregating data. Since time series operations often involve reading a single column of data at a time, column-oriented storage reduces the amount of disk I/O and allows for efficient data compression.

How do time series databases optimize for data compression?

Time series databases optimize for data compression by taking advantage of the fact that many values within a column are similar over a short period. They use encoding techniques such as run-length or bitmap encoding to reduce the amount of storage space required.

What are some common operations performed on time series data?

Common operations on time series data include writes to recent time intervals, reads that typically access data from the same timestamp and data source combination, and deletes that involve removing older data that is no longer needed.

How does the chunking design in time series databases improve delete operations?

The chunking design improves delete operations by allowing for the deletion of entire chunks of data at once, rather than individual key-value pairs. This is more efficient because it avoids the need to update multiple indexes or in-memory buffers, and it simplifies the deletion process by removing entire files or chunks.