The `MergeTree` engine and other engines of this family (`*MergeTree`) are the most robust ClickHousе table engines.
The basic idea for `MergeTree` engines family is the following. When you have tremendous amount of a data that should be inserted into the table, you should write them quickly part by part and then merge parts by some rules in background. This method is much more efficient than constantly rewriting data in the storage at the insert.
Main features:
- Stores data sorted by primary key.
This allows you to create a small sparse index that helps find data faster.
ClickHouse supports certain operations with partitions that are more effective than general operations on the same data with the same result. ClickHouse also automatically cuts off the partition data where the partitioning key is specified in the query. This also increases the query performance.
-`PARTITION BY` — The [partitioning key](custom_partitioning_key.md).
For partitioning by month, use the `toYYYYMM(date_column)` expression, where `date_column` is a column with a date of the type [Date](../../data_types/date.md). The partition names here have the `"YYYYMM"` format.
-`SETTINGS` — Additional parameters that control the behavior of the `MergeTree`:
-`index_granularity` — The granularity of an index. The number of data rows between the "marks" of an index. By default, 8192.
**Example of sections setting**
```
ENGINE MergeTree() PARTITION BY toYYYYMM(EventDate) ORDER BY (CounterID, EventDate, intHash32(UserID)) SAMPLE BY intHash32(UserID) SETTINGS index_granularity=8192
We also set an expression for sampling as a hash by the user ID. This allows you to pseudorandomize the data in the table for each `CounterID` and `EventDate`. If, when selecting the data, you define a [SAMPLE](../../query_language/select.md#select-sample-clause) clause, ClickHouse will return an evenly pseudorandom data sample for a subset of users.
-`date-column` — The name of a column of the type [Date](../../data_types/date.md). ClickHouse automatically creates partitions by month on the basis of this column. The partition names are in the `"YYYYMM"` format.
-`(primary, key)` — primary key. Type — [Tuple()](../../data_types/tuple.md- `index_granularity` — The granularity of an index. The number of data rows between the "marks" of an index. The value 8192 is appropriate for most tasks.
The `MergeTree` engine is configured in the same way as in the example above for the main engine configuration method.
</details>
## Data Storage
A table consists of data *parts* sorted by primary key.
When data is inserted in a table, separate data parts are created and each of them is lexicographically sorted by primary key. For example, if the primary key is `(CounterID, Date)`, the data in the part is sorted by `CounterID`, and within each `CounterID`, it is ordered by `Date`.
Data belonging to different partitions are separated into different parts. In the background, ClickHouse merges data parts for more efficient storage. Parts belonging to different partitions are not merged. The merge mechanism does not guarantee that all rows with the same primary key will be in the same data part.
For each data part, ClickHouse creates an index file that contains the primary key value for each index row ("mark"). Index row numbers are defined as `n * index_granularity`. The maximum value `n` is equal to the integer part of dividing the total number of rows by the `index_granularity`. For each column, the "marks" are also written for the same index rows as the primary key. These "marks" allow you to find the data directly in the columns.
You can use a single large table and continually add data to it in small chunks – this is what the `MergeTree` engine is intended for.
## Primary Keys and Indexes in Queries
Let's take the `(CounterID, Date)` primary key. In this case, the sorting and index can be illustrated as follows:
The examples above show that it is always more effective to use an index than a full scan.
A sparse index allows extra strings to be read. When reading a single range of the primary key, up to `index_granularity * 2` extra rows in each data block can be read. In most cases, ClickHouse performance does not degrade when `index_granularity = 8192`.
Sparse indexes allow you to work with a very large number of table rows, because such indexes are always stored in the computer's RAM.
ClickHouse does not require a unique primary key. You can insert multiple rows with the same primary key.
### Selecting the Primary Key
The number of columns in the primary key is not explicitly limited. Depending on the data structure, you can include more or fewer columns in the primary key. This may:
- Improve the performance of an index.
If the primary key is `(a, b)`, then adding another column `c` will improve the performance if the following conditions are met:
- There are queries with a condition on column `c`.
- Long data ranges (several times longer than the `index_granularity`) with identical values for `(a, b)` are common. In other words, when adding another column allows you to skip quite long data ranges.
- Improve data compression.
ClickHouse sorts data by primary key, so the higher the consistency, the better the compression.
- Provide additional logic when data parts merging in the [CollapsingMergeTree](collapsingmergetree.md#table_engine-collapsingmergetree) and [SummingMergeTree](summingmergetree.md) engines.
A long primary key will negatively affect the insert performance and memory consumption, but extra columns in the primary key do not affect ClickHouse performance during `SELECT` queries.
For`SELECT` queries, ClickHouse analyzes whether an index can be used. An index can be used if the `WHERE/PREWHERE` clause has an expression (as one of the conjunction elements, or entirely) that represents an equality or inequality comparison operation, or if it has `IN` or `LIKE` with a fixed prefix on columns or expressions that are in the primary key or partitioning key, or on certain partially repetitive functions of these columns, or logical relationships of these expressions.
Thus, it is possible to quickly run queries on one or many ranges of the primary key. In this example, queries will be fast when run for a specific tracking tag; for a specific tag and date range; for a specific tag and date; for multiple tags with a date range, and so on.
Let's look at the engine configured as follows:
```
ENGINE MergeTree() PARTITION BY toYYYYMM(EventDate) ORDER BY (CounterID, EventDate) SETTINGS index_granularity=8192
```
In this case, in queries:
``` sql
SELECT count() FROM table WHERE EventDate = toDate(now()) AND CounterID = 34
SELECT count() FROM table WHERE EventDate = toDate(now()) AND (CounterID = 34 OR CounterID = 42)
SELECT count() FROM table WHERE ((EventDate >= toDate('2014-01-01') AND EventDate <= toDate('2014-01-31')) OR EventDate = toDate('2014-05-01')) AND CounterID IN (101500, 731962, 160656) AND (CounterID = 101500 OR EventDate != toDate('2014-05-01'))
```
ClickHouse will use the primary key index to trim improper data and the monthly partitioning key to trim partitions that are in improper date ranges.
The queries above show that the index is used even for complex expressions. Reading from the table is organized so that using the index can't be slower than a full scan.
In the example below, the index can't be used.
``` sql
SELECT count() FROM table WHERE CounterID = 34 OR URL LIKE '%upyachka%'
To check whether ClickHouse can use the index when running a query, use the settings [force_index_by_date](../settings/settings.md#settings-force_index_by_date) and [force_primary_key](../settings/settings.md).
The key for partitioning by month allows reading only those data blocks which contain dates from the proper range. In this case, the data block may contain data for many dates (up to an entire month). Within a block, data is sorted by primary key, which might not contain the date as the first column. Because of this, using a query with only a date condition that does not specify the primary key prefix will cause more data to be read than for a single date.
## Concurrent Data Access
For concurrent table access, we use multi-versioning. In other words, when a table is simultaneously read and updated, data is read from a set of parts that is current at the time of the query. There are no lengthy locks. Inserts do not get in the way of read operations.
Reading from a table is automatically parallelized.
