Column-level encoding(s)

Column-level encoding is a technique used to compress and optimize data storage in a database. It involves encoding the data in each column of a table separately, rather than encoding the entire table as a whole. This allows for more efficient storage and retrieval of the data, as well as faster query processing.

Several types of column-level encoding can be used, including:

• Run-length encoding (RLE): This is a simple form of encoding that represents a sequence of identical values with a single value and a count.

• Dictionary encoding: This uses a dictionary of unique values to represent all the values in a column. Each value is replaced with its corresponding dictionary index, which is a smaller integer value.

• Bit-packing: This involves packing multiple values into a single machine word, which reduces the storage space required for each value.

• Delta encoding: This involves storing the difference between consecutive values in a column, which can be more efficient for columns with many similar values.

Column-level encoding is particularly useful for columns that contain a high degree of duplication or have a limited number of distinct values, such as columns with enum or boolean data types. Additionally, it can be used on columns that have high selectivity, i.e columns that are frequently used in WHERE clauses.

Run-length encoding (RLE)

Run-length encoding (RLE) is a data compression technique that is used to reduce the number of repeating elements in a data stream or sequence. It works by replacing a sequence of repeating elements with a single element and a count of the number of times it occurs.

For example, consider the following sequence of numbers:

1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 4, 4

Using RLE, we can compress this sequence into the following format:

(1, 3), (2, 4), (3, 3), (4, 3)

Each tuple in the compressed sequence represents a single element and the number of times it occurs. In this case, the number 1 appears 3 times, the number 2 appears 4 times, the number 3 appears 3 times, and the number 4 appears 3 times.

Here is a MermaidJS diagram that illustrates the process of RLE:

The above diagram illustrates the process of RLE where the data stream is sent to the RLE encoder and the encoder compresses the data stream and sends it to the compressed data stream.

RLE is commonly used in image compression, video compression, and file compression to reduce the size of data without losing any significant information. It's a simple but effective technique for reducing the size of data with repeating elements.

Dictionary encoding

Dictionary encoding is a method of compressing data by replacing repetitive sequences of characters with unique codes. This method is commonly used in text compression and image compression.

For example, imagine we have a text document that contains the following sentence: "The quick brown fox jumps over the lazy dog."

Using dictionary encoding, we can replace the repetitive words with unique codes. For example, we can replace "the" with code "1", "quick" with code "2", "brown" with code "3", and so on. Our sentence would now look like: "1 quick 3 fox jumps over 1 lazy dog."

Now we can create a dictionary that maps each code to its corresponding word. This dictionary would look like:

1: "the" 2: "quick" 3: "brown" 4: "fox" 5: "jumps" 6: "over" 7: "lazy" 8: "dog"

When we want to decompress the text, we can use the dictionary to replace the codes with the corresponding words.

Here is a Mermaidjs representation of the process:

In this diagram, the original text is first converted to a compressed text using dictionary encoding and a dictionary is created. The compressed text and dictionary can then be used to decompress the text back to its original form.

Bit-packing

Bit-packing is a technique used in computer science and data compression to store multiple smaller data elements in a single larger unit, using a fixed number of bits for each element. This allows for efficient storage and retrieval of data, as well as reducing the overall storage space required.

For example, consider a scenario where you want to store a set of integers, each ranging from 0 to 15. Instead of using a full 4 bytes (32 bits) for each integer, you can use only 4 bits (1/8th of a byte) for each integer by packing 8 integers into a single 32-bit word.

This means that instead of needing 8 separate 4-byte integers to store the data, you only need 1 32-bit word. This can be very useful in situations where memory or storage space is limited, such as embedded systems or mobile devices.

It is important to note that bit-packing can also be used for other types of data, such as characters, booleans, and even floating-point numbers. The key is to use the minimum number of bits necessary to represent each element, while still being able to easily retrieve and use the data.

Delta encoding

Delta encoding is a method of compressing data by storing the difference between consecutive values instead of the actual values themselves. This is particularly useful when working with data that has a lot of repeating values or patterns.

For example, let's say we have a set of data that represents the temperature in a city over the course of a week. The data looks like this:

Using delta encoding, we would store the difference between each day's temperature and the previous day's temperature, rather than the actual temperature. The encoded data would look like this:

This encoded data takes up less space than the original data because there are fewer unique values. To decode the data, we simply add the encoded values to the previous day's temperature.

In this example, we can see that the original data has 7 unique values, while the encoded data only has 5 unique values. This means that the encoded data takes up less space and can be transmitted more efficiently. The decoding process simply involves adding the encoded values to the previous day's temperature to get the original data back.