ClickHouse/docs/en/operations/table_engines/collapsingmergetree.md

CollapsingMergeTree

The engine inherits from MergeTree and adds the logic of rows collapsing to data parts merge algorithm.

CollapsingMergeTree asynchronously deletes (collapses) pairs of rows if all of the fields in a sorting key (ORDER BY) are equivalent excepting the particular field Sign which can have 1 and -1 values. Rows without a pair are kept. For more details see the Collapsing section of the document.

The engine may significantly reduce the volume of storage and increase efficiency of SELECT query as a consequence.

Creating a Table

CREATE TABLE [IF NOT EXISTS] [db.]table_name [ON CLUSTER cluster]
(
    name1 [type1] [DEFAULT|MATERIALIZED|ALIAS expr1],
    name2 [type2] [DEFAULT|MATERIALIZED|ALIAS expr2],
    ...
) ENGINE = CollapsingMergeTree(sign)
[PARTITION BY expr]
[ORDER BY expr]
[SAMPLE BY expr]
[SETTINGS name=value, ...]

For a description of query parameters, see query description.

CollapsingMergeTree Parameters

• sign — Name of the column with the type of row: 1 is a "state" row, -1 is a "cancel" row.

  Column data type — Int8.

Query clauses

When creating a CollapsingMergeTree table, the same query clauses are required, as when creating a MergeTree table.

Deprecated Method for Creating a Table

!!! attention Do not use this method in new projects and, if possible, switch the old projects to the method described above.

CREATE TABLE [IF NOT EXISTS] [db.]table_name [ON CLUSTER cluster]
(
    name1 [type1] [DEFAULT|MATERIALIZED|ALIAS expr1],
    name2 [type2] [DEFAULT|MATERIALIZED|ALIAS expr2],
    ...
) ENGINE [=] CollapsingMergeTree(date-column [, sampling_expression], (primary, key), index_granularity, sign)

All of the parameters excepting sign have the same meaning as in MergeTree.

• sign — Name of the column with the type of row: 1 — "state" row, -1 — "cancel" row.

  Column Data Type — Int8.

Collapsing

Data

Consider the situation where you need to save continually changing data for some object. It sounds logical to have one row for an object and update it at any change, but update operation is expensive and slow for DBMS because it requires rewriting of the data in the storage. If you need to write data quickly, update not acceptable, but you can write the changes of an object sequentially as follows.

Use the particular column Sign. If Sign = 1 it means that the row is a state of an object, let's call it "state" row. If Sign = -1 it means the cancellation of the state of an object with the same attributes, let's call it "cancel" row.

For example, we want to calculate how much pages users checked at some site and how long they were there. At some moment of time we write the following row with the state of user activity:

┌──────────────UserID─┬─PageViews─┬─Duration─┬─Sign─┐
│ 4324182021466249494 │         5 │      146 │    1 │
└─────────────────────┴───────────┴──────────┴──────┘

At some moment later we register the change of user activity and write it with the following two rows.

┌──────────────UserID─┬─PageViews─┬─Duration─┬─Sign─┐
│ 4324182021466249494 │         5 │      146 │   -1 │
│ 4324182021466249494 │         6 │      185 │    1 │
└─────────────────────┴───────────┴──────────┴──────┘

The first row cancels the previous state of the object (user). It should copy the sorting key fields of the canceled state excepting Sign.

The second row contains the current state.

As we need only the last state of user activity, the rows

┌──────────────UserID─┬─PageViews─┬─Duration─┬─Sign─┐
│ 4324182021466249494 │         5 │      146 │    1 │
│ 4324182021466249494 │         5 │      146 │   -1 │
└─────────────────────┴───────────┴──────────┴──────┘

can be deleted collapsing the invalid (old) state of an object. CollapsingMergeTree does this while merging of the data parts.

Why we need 2 rows for each change read in the Algorithm paragraph.

Peculiar properties of such approach

1. The program that writes the data should remember the state of an object to be able to cancel it. "Cancel" string should contain copies of the sorting key fields of the "state" string and the opposite Sign. It increases the initial size of storage but allows to write the data quickly.
2. Long growing arrays in columns reduce the efficiency of the engine due to load for writing. The more straightforward data, the higher efficiency.
3. The SELECT results depend strongly on the consistency of object changes history. Be accurate when preparing data for inserting. You can get unpredictable results in inconsistent data, for example, negative values for non-negative metrics such as session depth.

Algorithm

When ClickHouse merges data parts, each group of consecutive rows with the same sorting key (ORDER BY) is reduced to not more than two rows, one with Sign = 1 ("state" row) and another with Sign = -1 ("cancel" row). In other words, entries collapse.

For each resulting data part ClickHouse saves:

1. The first "cancel" and the last "state" rows, if the number of "state" and "cancel" rows matches.

2. The last "state" row, if there is one more "state" row than "cancel" rows.

3. The first "cancel" row, if there is one more "cancel" row than "state" rows.

4. None of the rows, in all other cases.

   The merge continues, but ClickHouse treats this situation as a logical error and records it in the server log. This error can occur if the same data were inserted more than once.

Thus, collapsing should not change the results of calculating statistics. Changes gradually collapsed so that in the end only the last state of almost every object left.

The Sign is required because the merging algorithm doesn't guarantee that all of the rows with the same sorting key will be in the same resulting data part and even on the same physical server. ClickHouse process SELECT queries with multiple threads, and it can not predict the order of rows in the result. The aggregation is required if there is a need to get completely "collapsed" data from CollapsingMergeTree table.

To finalize collapsing, write a query with GROUP BY clause and aggregate functions that account for the sign. For example, to calculate quantity, use sum(Sign) instead of count(). To calculate the sum of something, use sum(Sign * x) instead of sum(x), and so on, and also add HAVING sum(Sign) > 0.

The aggregates count, sum and avg could be calculated this way. The aggregate uniq could be calculated if an object has at least one state not collapsed. The aggregates min and max could not be calculated because CollapsingMergeTree does not save values history of the collapsed states.

If you need to extract data without aggregation (for example, to check whether rows are present whose newest values match certain conditions), you can use the FINAL modifier for the FROM clause. This approach is significantly less efficient.

Example of use

Example data:

┌──────────────UserID─┬─PageViews─┬─Duration─┬─Sign─┐
│ 4324182021466249494 │         5 │      146 │    1 │
│ 4324182021466249494 │         5 │      146 │   -1 │
│ 4324182021466249494 │         6 │      185 │    1 │
└─────────────────────┴───────────┴──────────┴──────┘

Creation of the table:

CREATE TABLE UAct
(
    UserID UInt64,
    PageViews UInt8,
    Duration UInt8,
    Sign Int8
)
ENGINE = CollapsingMergeTree(Sign)
ORDER BY UserID

Insertion of the data:

INSERT INTO UAct VALUES (4324182021466249494, 5, 146, 1)

INSERT INTO UAct VALUES (4324182021466249494, 5, 146, -1),(4324182021466249494, 6, 185, 1)

We use two INSERT queries to create two different data parts. If we insert the data with one query ClickHouse creates one data part and will not perform any merge ever.

Getting the data:

SELECT * FROM UAct

┌──────────────UserID─┬─PageViews─┬─Duration─┬─Sign─┐
│ 4324182021466249494 │         5 │      146 │   -1 │
│ 4324182021466249494 │         6 │      185 │    1 │
└─────────────────────┴───────────┴──────────┴──────┘
┌──────────────UserID─┬─PageViews─┬─Duration─┬─Sign─┐
│ 4324182021466249494 │         5 │      146 │    1 │
└─────────────────────┴───────────┴──────────┴──────┘

What do we see and where is collapsing?

With two INSERT queries, we created 2 data parts. The SELECT query was performed in 2 threads, and we got a random order of rows. Collapsing not occurred because there was no merge of the data parts yet. ClickHouse merges data part in an unknown moment of time which we can not predict.

Thus we need aggregation:

SELECT
    UserID,
    sum(PageViews * Sign) AS PageViews,
    sum(Duration * Sign) AS Duration
FROM UAct
GROUP BY UserID
HAVING sum(Sign) > 0

┌──────────────UserID─┬─PageViews─┬─Duration─┐
│ 4324182021466249494 │         6 │      185 │
└─────────────────────┴───────────┴──────────┘

If we do not need aggregation and want to force collapsing, we can use FINAL modifier for FROM clause.

SELECT * FROM UAct FINAL

┌──────────────UserID─┬─PageViews─┬─Duration─┬─Sign─┐
│ 4324182021466249494 │         6 │      185 │    1 │
└─────────────────────┴───────────┴──────────┴──────┘

This way of selecting the data is very inefficient. Don't use it for big tables.

Example of another approach

Example data:

┌──────────────UserID─┬─PageViews─┬─Duration─┬─Sign─┐
│ 4324182021466249494 │         5 │      146 │    1 │
│ 4324182021466249494 │        -5 │     -146 │   -1 │
│ 4324182021466249494 │         6 │      185 │    1 │
└─────────────────────┴───────────┴──────────┴──────┘

The idea is that merges take into account only key fields. And in the "Cancel" line we can specify negative values that equalize the previous version of the row when summing without using the Sign column. For this approach, it is necessary to change the data type PageViews, Duration to store negative values of UInt8 -> Int16.

CREATE TABLE UAct
(
    UserID UInt64,
    PageViews Int16,
    Duration Int16,
    Sign Int8
)
ENGINE = CollapsingMergeTree(Sign)
ORDER BY UserID

Let's test the approach:

insert into UAct values(4324182021466249494,  5,  146,  1);
insert into UAct values(4324182021466249494, -5, -146, -1);
insert into UAct values(4324182021466249494,  6,  185,  1);

select * from UAct final; // avoid using final in production (just for a test or small tables)
┌──────────────UserID─┬─PageViews─┬─Duration─┬─Sign─┐
│ 4324182021466249494 │         6 │      185 │    1 │
└─────────────────────┴───────────┴──────────┴──────┘

SELECT
    UserID,
    sum(PageViews) AS PageViews,
    sum(Duration) AS Duration
FROM UAct
GROUP BY UserID
┌──────────────UserID─┬─PageViews─┬─Duration─┐
│ 4324182021466249494 │         6 │      185 │
└─────────────────────┴───────────┴──────────┘

select count() FROM UAct
┌─count()─┐
│       3 │
└─────────┘

optimize table UAct final;

select * FROM UAct
┌──────────────UserID─┬─PageViews─┬─Duration─┬─Sign─┐
│ 4324182021466249494 │         6 │      185 │    1 │
└─────────────────────┴───────────┴──────────┴──────┘

Original article