<a name="table_engine-collapsingmergetree"></a>

# CollapsingMergeTree

The engine inherits from [MergeTree](mergetree.md) 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 row 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](#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

```sql
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](../../query_language/create.md).

**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 [clauses](mergetree.md) are required, as when creating a `MergeTree` table.

<details markdown="1"><summary>Deprecated Method for Creating a Table</summary>

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

```sql
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`.
</details>


## 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` when writing row. 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 all of the 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 be the copy of "state" string with 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. `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 primary key 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.
  1. The last "state" row, if there is one more "state" row than "cancel" rows.
  1. The first "cancel" row, if there is one more "cancel" row than "state" rows.
  1. 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 primary 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 list 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:

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

Insertion of the data:

```sql
INSERT INTO UAct VALUES (4324182021466249494, 5, 146, 1)
```
```sql
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:

```sql
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.

```sql
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.

[Original article](https://clickhouse.yandex/docs/en/operations/table_engines/collapsingmergetree/) <!--hide-->
