Returns a string with the name of the host that this function was performed on. For distributed processing, this is the name of the remote server host, if the function is performed on a remote server.
If `NULL` is passed to the function as input, then it returns the `Nullable(Nothing)` type, which corresponds to an internal `NULL` representation in ClickHouse.
In ClickHouse, queries are always run on blocks (sets of column parts). This function allows getting the size of the block that you called it for.
## materialize(x)
Turns a constant into a full column containing just one value.
In ClickHouse, full columns and constants are represented differently in memory. Functions work differently for constant arguments and normal arguments (different code is executed), although the result is almost always the same. This function is for debugging this behavior.
Accepts constant strings: database name, table name, and column name. Returns a UInt8 constant expression equal to 1 if there is a column, otherwise 0. If the hostname parameter is set, the test will run on a remote server.
The function throws an exception if the table does not exist.
For elements in a nested data structure, the function checks for the existence of a column. For the nested data structure itself, the function returns 0.
If the 'x' value is equal to one of the elements in the 'array_from' array, it returns the existing element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'default'. If there are multiple matching elements in 'array_from', it returns one of the matches.
Differs from the first variation in that the 'default' argument is omitted.
If the 'x' value is equal to one of the elements in the 'array_from' array, it returns the matching element (that is numbered the same) from the 'array_to' array. Otherwise, it returns 'x'.
Same as for [runningDifference](./other_functions.md#other_functions-runningdifference), the difference is the value of the first row, returned the value of the first row, and each subsequent row returns the difference from the previous row.
Accepts a UInt64 number. Interprets it as a MAC address in big endian. Returns a string containing the corresponding MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form).
## MACStringToNum(s)
The inverse function of MACNumToString. If the MAC address has an invalid format, it returns 0.
## MACStringToOUI(s)
Accepts a MAC address in the format AA:BB:CC:DD:EE:FF (colon-separated numbers in hexadecimal form). Returns the first three octets as a UInt64 number. If the MAC address has an invalid format, it returns 0.
Outputs data in the range selected by the index without filtering by the expression specified as an argument.
The expression passed to the function is not calculated, but ClickHouse applies the index to this expression in the same way as if the expression was in the query without `indexHint`.
The table has indexes for the fields `(FlightDate, (Year, FlightDate))`.
Create a selection by date like this:
```
:) SELECT FlightDate AS k, count() FROM ontime GROUP BY k ORDER BY k
SELECT
FlightDate AS k,
count()
FROM ontime
GROUP BY k
ORDER BY k ASC
┌──────────k─┬─count()─┐
│ 2017-01-01 │ 13970 │
│ 2017-01-02 │ 15882 │
........................
│ 2017-09-28 │ 16411 │
│ 2017-09-29 │ 16384 │
│ 2017-09-30 │ 12520 │
└────────────┴─────────┘
273 rows in set. Elapsed: 0.072 sec. Processed 4.28 million rows, 8.55 MB (59.00 million rows/s., 118.01 MB/s.)
```
In this selection, the index is not used and ClickHouse processed the entire table (`Processed 4.28 million rows`). To apply the index, select a specific date and run the following query:
```
:) SELECT FlightDate AS k, count() FROM ontime WHERE k = '2017-09-15' GROUP BY k ORDER BY k
SELECT
FlightDate AS k,
count()
FROM ontime
WHERE k = '2017-09-15'
GROUP BY k
ORDER BY k ASC
┌──────────k─┬─count()─┐
│ 2017-09-15 │ 16428 │
└────────────┴─────────┘
1 rows in set. Elapsed: 0.014 sec. Processed 32.74 thousand rows, 65.49 KB (2.31 million rows/s., 4.63 MB/s.)
```
The last line of output shows that by using the index, ClickHouse processed a significantly smaller number of rows (`Processed 32.74 thousand rows`).
Now pass the expression `k = '2017-09-15'` to the `indexHint` function:
```
:) SELECT FlightDate AS k, count() FROM ontime WHERE indexHint(k = '2017-09-15') GROUP BY k ORDER BY k
SELECT
FlightDate AS k,
count()
FROM ontime
WHERE indexHint(k = '2017-09-15')
GROUP BY k
ORDER BY k ASC
┌──────────k─┬─count()─┐
│ 2017-09-14 │ 7071 │
│ 2017-09-15 │ 16428 │
│ 2017-09-16 │ 1077 │
│ 2017-09-30 │ 8167 │
└────────────┴─────────┘
4 rows in set. Elapsed: 0.004 sec. Processed 32.74 thousand rows, 65.49 KB (8.97 million rows/s., 17.94 MB/s.)
```
The response to the request shows that ClickHouse applied the index in the same way as the previous time (`Processed 32.74 thousand rows`). However, the resulting set of rows shows that the expression `k = '2017-09-15'` was not used when generating the result.
Because the index is sparse in ClickHouse, "extra" data ends up in the response when reading a range (in this case, the adjacent dates). Use the `indexHint` function to see it.
## replicate
Creates an array with a single value.
Used for internal implementation of [arrayJoin](array_join.md#functions_arrayjoin).
```
replicate(x, arr)
```
**Parameters:**
-`arr` — Original array. ClickHouse creates a new array of the same length as the original and fills it with the value `x`.
-`x` — The value that the resulting array will be filled with.
Returns the remaining space information of the disk, in bytes. This information is evaluated using the configured by path.
## filesystemCapacity
Returns the capacity information of the disk, in bytes. This information is evaluated using the configured by path.
## finalizeAggregation
Takes state of aggregate function. Returns result of aggregation (finalized state).
## runningAccumulate
Takes the states of the aggregate function and returns a column with values, are the result of the accumulation of these states for a set of block lines, from the first to the current line.
For example, takes state of aggregate function (example runningAccumulate(uniqState(UserID))), and for each row of block, return result of aggregate function on merge of states of all previous rows and current row.
So, result of function depends on partition of data to blocks and on order of data in block.
