TimescaleDB uses different compression algorithms, depending on the data type that is being compressed.

For integers, timestamps, and other integer-like types, a combination of compression methods are used: delta encoding, delta-of-delta, simple-8b, and run-length encoding.

For columns that do not have a high amount of repeated values, XOR-based compression is used, with some dictionary compression.

For all other types, dictionary compression is used.

For integers, timestamps, and other integer-like types TimescaleDB uses a combination of delta encoding, delta-of-delta, simple 8-b, and run-length encoding.

The simple-8b compression method has been extended so that data can be decompressed in reverse order. Backward scanning queries are common in time-series workloads. This means that these types of queries run much faster.

Delta encoding reduces the amount of information required to represent a data object by only storing the difference, sometimes referred to as the delta, between that object and one or more reference objects. These algorithms work best where there is a lot of redundant information, and it is often used in workloads like versioned file systems. For example, this is how Dropbox keeps your files synchronized. Applying delta-encoding to time-series data means that you can use fewer bytes to represent a data point, because you only need to store the delta from the previous data point.

For example, imagine you had a dataset that collected CPU, free memory, temperature, and humidity over time. If you time column was stored as an integer value, like seconds since UNIX epoch, your raw data would look a little like this:

time | cpu | mem_free_bytes | temperature | humidity |
---|---|---|---|---|

2023-04-01 10:00:00 | 82 | 1,073,741,824 | 80 | 25 |

2023-04-01 10:05:00 | 98 | 858,993,459 | 81 | 25 |

2023-04-01 10:05:00 | 98 | 858,904,583 | 81 | 25 |

With delta encoding, you only need to store how much each value changed from the previous data point, resulting in smaller values to store. So after the first row, you can represent subsequent rows with less information, like this:

time | cpu | mem_free_bytes | temperature | humidity |
---|---|---|---|---|

2020-04-01 10:00:00 | 82 | 1,073,741,824 | 80 | 25 |

5 seconds | 16 | -214,748,365 | 1 | 0 |

5 seconds | 0 | -88,876 | 0 | 0 |

Applying delta encoding to time-series data takes advantage of the fact that most time-series datasets are not random, but instead represent something that is slowly changing over time. The storage savings over millions of rows can be substantial, especially if the value changes very little, or doesn't change at all.

Delta-of-delta encoding takes delta encoding one step further and applies delta-encoding over data that has previously been delta-encoded. With time-series datasets where data collection happens at regular intervals, you can apply delta-of-delta encoding to the time column, which results in only needing to store a series of zeroes.

In other words, delta encoding stores the first derivative of the dataset, while delta-of-delta encoding stores the second derivative of the dataset.

Applied to the example dataset from earlier, delta-of-delta encoding results in this:

time | cpu | mem_free_bytes | temperature | humidity |
---|---|---|---|---|

2020-04-01 10:00:00 | 82 | 1,073,741,824 | 80 | 25 |

5 seconds | 16 | -214,748,365 | 1 | 0 |

0 | 0 | -88,876 | 0 | 0 |

In this example, delta-of-delta further compresses 5 seconds in the time column down to 0 for every entry in the time column after the second row, because the five second gap remains constant for each entry. Note that you see two entries in the table before the delta-delta 0 values, because you need two deltas to compare.

This compresses a full timestamp of 8 bytes, or 64 bits, down to just a single bit, resulting in 64x compression.

With delta and delta-of-delta encoding, you can significantly reduce the number of digits you need to store. But you still need an efficient way to store the smaller integers. The previous examples used a standard integer datatype for the time column, which needs 64 bits to represent the value of 0 when delta-delta encoded. This means that even though you are only storing the integer 0, you are still consuming 64 bits to store it, so you haven't actually saved anything.

Simple-8b is one of the simplest and smallest methods of storing variable-length integers. In this method, integers are stored as a series of fixed-size blocks. For each block, every integer within the block is represented by the minimal bit-length needed to represent the largest integer in that block. The first bits of each block denotes the minimum bit-length for the block.

This technique has the advantage of only needing to store the length once for a given block, instead of once for each integer. Because the blocks are of a fixed size, you can infer the number of integers in each block from the size of the integers being stored.

For example, if you wanted to store a temperature that changed over time, and you applied delta encoding, you might end up needing to store this set of integers:

temperature (deltas) |
---|

1 |

10 |

11 |

13 |

9 |

100 |

22 |

11 |

With a block size of 10 digits, you could store this set of integers as two blocks: one block storing 5 2-digit numbers, and a second block storing 3 3-digit numbers, like this:

{2: [01, 10, 11, 13, 09]} {3: [100, 022, 011]}

In this example, both blocks store about 10 digits worth of data, even though some of the numbers have to be padded with a leading 0. You might also notice that the second block only stores 9 digits, because 10 is not evenly divisible by 3.

Simple-8b works in this way, except it uses binary numbers instead of decimal, and it usually uses 64-bit blocks. In general, the longer the integer, the fewer number of integers that can be stored in each block.

Simple-8b compresses integers very well, however, if you have a large number of repeats of the same value, you can get even better compression with run-length encoding. This method works well for values that don't change very often, or if an earlier transformation removes the changes.

Run-length encoding is one of the classic compression algorithms. For time-series data with billions of contiguous zeroes, or even a document with a million identically repeated strings, run-length encoding works incredibly well.

For example, if you wanted to store a temperature that changed minimally over time, and you applied delta encoding, you might end up needing to store this set of integers:

temperature (deltas) |
---|

11 |

12 |

12 |

12 |

12 |

12 |

12 |

1 |

12 |

12 |

12 |

12 |

For values like these, you do not need to store each instance of the value, but
rather how long the run, or number of repeats, is. You can store this set of
numbers as `{run; value}`

pairs like this:

{1; 11}, {6; 12}, {1; 1}, {4; 12}

This technique uses 11 digits of storage (1, 1, 1, 6, 1, 2, 1, 1, 4, 1, 2), rather than 23 digits that an optimal series of variable-length integers requires (11, 12, 12, 12, 12, 12, 12, 1, 12, 12, 12, 12).

Run-length encoding is also used as a building block for many more advanced algorithms, such as Simple-8b RLE, which is an algorithm that combines run-length and Simple-8b techniques. TimescaleDB implements a variant of Simple-8b RLE. This variant uses different sizes to standard Simple-8b, in order to handle 64-bit values, and RLE.

For columns that do not have a high amount of repeated values, TimescaleDB uses XOR-based compression.

The standard XOR-based compression method has been extended so that data can be decompressed in reverse order. Backward scanning queries are common in time-series workloads. This means that queries that use backwards scans run much faster.

Floating point numbers are usually more difficult to compress than integers. Fixed-length integers often have leading zeroes, but floating point numbers usually use all of their available bits, especially if they are converted from decimal numbers, which can't be represented precisely in binary.

Techniques like delta-encoding don't work well for floats, because they do not reduce the number of bits sufficiently. This means that most floating-point compression algorithms tend to be either complex and slow, or truncate significant digits. One of the few simple and fast lossless floating-point compression algorithms is XOR-based compression, built on top of Facebook's Gorilla compression.

XOR is the binary function `exclusive or`

. In this algorithm, successive
floating point numbers are compared with XOR, and a difference results in a bit
being stored. The first data point is stored without compression, and subsequent
data points are represented using their XOR'd values.

For values that are not integers or floating point, TiemscaleDB uses dictionary compression.

One of the earliest lossless compression algorithms, dictionary compression is the basis of many popular compression methods. Dictionary compression can also be found in areas outside of computer science, such as medical coding.

Instead of storing values directly, dictionary compression works by making a list of the possible values that can appear, and then storing an index into a dictionary containing the unique values. This technique is quite versatile, can be used regardless of data type, and works especially well when you have a limited set of values that repeat frequently.

For example, if you had the list of temperatures shown earlier, but you wanted an additional column storing a city location for each measurement, you might have a set of values like this:

City |
---|

New York |

San Francisco |

San Francisco |

Los Angeles |

Instead of storing all the city names directly, you can instead store a dictionary, like this:

{0: "New York", 1: "San Francisco", 2: "Los Angeles",}

You can then store just the indices in your column, like this:

City |
---|

0 |

1 |

1 |

2 |

For a dataset with a lot of repetition, this can offer significant compression. In the example, each city name is on average 11 bytes in length, while the indices are never going to be more than 4 bytes long, reducing space usage nearly 3 times. In TimescaleDB, the list of indices is compressed even further with the Simple-8b+RLE method, making the storage cost even smaller.

Dictionary compression doesn't always result in savings. If your dataset doesn't have a lot of repeated values, then the dictionary is the same size as the original data. TimescaleDB automatically detects this case, and falls back to not using a dictionary in that scenario.

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