Internals:
https://github.com/ClickHouse/clickhouse-presentations/blob/master/meetup41/merge_tree.pdf
This is the multi-page printable view of this section. Click here to print.
MergeTree table engine family
MergeTree table engine family
- 1: CollapsingMergeTree vs ReplacingMergeTree
- 2: Part names & MVCC
- 3: How to pick an ORDER BY / PRIMARY KEY / PARTITION BY for the MergeTree-family table
- 4: AggregatingMergeTree
- 5: index & column files
- 6: Merge performance and OPTIMIZE FINAL
- 7: Nulls in order by
- 8: ReplacingMergeTree
- 9: Skip index
- 10: SummingMergeTree
1 - CollapsingMergeTree vs ReplacingMergeTree
CollapsingMergeTree vs ReplacingMergeTree.
CollapsingMergeTree vs ReplacingMergeTree
| ReplacingMergeTree | CollapsingMergeTree |
|---|---|
| + very easy to use (always replace) | - more complex (accounting-alike, put ‘rollback’ records to fix something) |
| + you don’t need to store the previous state of the row | - you need to the store (somewhere) the previous state of the row, OR extract it from the table itself (point queries is not nice for ClickHouse) |
| - no deletes | + support deletes |
| - w/o FINAL - you can can always see duplicates, you need always to ‘pay’ FINAL performance penalty | + properly crafted query can give correct results without final (i.e. sum(amount * sign) will be correct, no matter of you have duplicated or not) |
- only uniq()-alike things can be calculated in materialied views | + you can do basic counts & sums in materialized views |
2 - Part names & MVCC
Part names & multiversion concurrency control.
Part names & multiversion concurrency control
Part name format is:
<partitionid>_<min_block_number>_<max_block_number>_<level>_<data_version>
system.parts contains all the information parsed.
partitionid is quite simple (it just comes from your partitioning key).
What are block_numbers?
DROP TABLE IF EXISTS part_names;
create table part_names (date Date, n UInt8, m UInt8) engine=MergeTree PARTITION BY toYYYYMM(date) ORDER BY n;
insert into part_names VALUES (now(), 0, 0);
select name, partition_id, min_block_number, max_block_number, level, data_version from system.parts where table = 'part_names' and active;
┌─name─────────┬─partition_id─┬─min_block_number─┬─max_block_number─┬─level─┬─data_version─┐
│ 202203_1_1_0 │ 202203 │ 1 │ 1 │ 0 │ 1 │
└──────────────┴──────────────┴──────────────────┴──────────────────┴───────┴──────────────┘
insert into part_names VALUES (now(), 0, 0);
select name, partition_id, min_block_number, max_block_number, level, data_version from system.parts where table = 'part_names' and active;
┌─name─────────┬─partition_id─┬─min_block_number─┬─max_block_number─┬─level─┬─data_version─┐
│ 202203_1_1_0 │ 202203 │ 1 │ 1 │ 0 │ 1 │
│ 202203_2_2_0 │ 202203 │ 2 │ 2 │ 0 │ 2 │
└──────────────┴──────────────┴──────────────────┴──────────────────┴───────┴──────────────┘
insert into part_names VALUES (now(), 0, 0);
select name, partition_id, min_block_number, max_block_number, level, data_version from system.parts where table = 'part_names' and active;
┌─name─────────┬─partition_id─┬─min_block_number─┬─max_block_number─┬─level─┬─data_version─┐
│ 202203_1_1_0 │ 202203 │ 1 │ 1 │ 0 │ 1 │
│ 202203_2_2_0 │ 202203 │ 2 │ 2 │ 0 │ 2 │
│ 202203_3_3_0 │ 202203 │ 3 │ 3 │ 0 │ 3 │
└──────────────┴──────────────┴──────────────────┴──────────────────┴───────┴──────────────┘
As you can see every insert creates a new incremental block_number which is written in part names both as <min_block_number> and <min_block_number> (and the level is 0 meaning that the part was never merged).
Those block numbering works in the scope of partition (for Replicated table) or globally across all partition (for plain MergeTree table).
ClickHouse always merge only continuous blocks . And new part names always refer to the minimum and maximum block numbers.
OPTIMIZE TABLE part_names;
┌─name─────────┬─partition_id─┬─min_block_number─┬─max_block_number─┬─level─┬─data_version─┐
│ 202203_1_3_1 │ 202203 │ 1 │ 3 │ 1 │ 1 │
└──────────────┴──────────────┴──────────────────┴──────────────────┴───────┴──────────────┘
As you can see here - three parts (with block number 1,2,3) were merged and they formed the new part with name 1_3 as min/max block size. Level get incremented.
Now even while previous (merged) parts still exists in filesystem for a while (as inactive) clickhouse is smart enough to understand that new part ‘covers’ same range of blocks as 3 parts of the prev ‘generation’
There might be a fifth section in the part name, data version.
Data version gets increased when a part mutates.
Every mutation takes one block number:
insert into part_names VALUES (now(), 0, 0);
insert into part_names VALUES (now(), 0, 0);
insert into part_names VALUES (now(), 0, 0);
select name, partition_id, min_block_number, max_block_number, level, data_version from system.parts where table = 'part_names' and active;
┌─name─────────┬─partition_id─┬─min_block_number─┬─max_block_number─┬─level─┬─data_version─┐
│ 202203_1_3_1 │ 202203 │ 1 │ 3 │ 1 │ 1 │
│ 202203_4_4_0 │ 202203 │ 4 │ 4 │ 0 │ 4 │
│ 202203_5_5_0 │ 202203 │ 5 │ 5 │ 0 │ 5 │
│ 202203_6_6_0 │ 202203 │ 6 │ 6 │ 0 │ 6 │
└──────────────┴──────────────┴──────────────────┴──────────────────┴───────┴──────────────┘
insert into part_names VALUES (now(), 0, 0);
alter table part_names update m=n where 1;
select name, partition_id, min_block_number, max_block_number, level, data_version from system.parts where table = 'part_names' and active;
┌─name───────────┬─partition_id─┬─min_block_number─┬─max_block_number─┬─level─┬─data_version─┐
│ 202203_1_3_1_7 │ 202203 │ 1 │ 3 │ 1 │ 7 │
│ 202203_4_4_0_7 │ 202203 │ 4 │ 4 │ 0 │ 7 │
│ 202203_5_5_0_7 │ 202203 │ 5 │ 5 │ 0 │ 7 │
│ 202203_6_6_0_7 │ 202203 │ 6 │ 6 │ 0 │ 7 │
│ 202203_8_8_0 │ 202203 │ 8 │ 8 │ 0 │ 8 │
└────────────────┴──────────────┴──────────────────┴──────────────────┴───────┴──────────────┘
OPTIMIZE TABLE part_names;
select name, partition_id, min_block_number, max_block_number, level, data_version from system.parts where table = 'part_names' and active;
┌─name───────────┬─partition_id─┬─min_block_number─┬─max_block_number─┬─level─┬─data_version─┐
│ 202203_1_8_2_7 │ 202203 │ 1 │ 8 │ 2 │ 7 │
└────────────────┴──────────────┴──────────────────┴──────────────────┴───────┴──────────────┘
3 - How to pick an ORDER BY / PRIMARY KEY / PARTITION BY for the MergeTree-family table
How to pick an ORDER BY / PRIMARY KEY / PARTITION BY for the MergeTree table.
How to pick an ORDER BY / PRIMARY KEY
Good order by usually have 3 to 5 columns, from lowest cardinal on the left (and the most important for filtering) to highest cardinal (and less important for filtering).
Practical approach to create an good ORDER BY for a table:
- Pick the columns you use in filtering always
- The most important for filtering and the lowest cardinal should be the left-most. Typically it’s something like
tenant_id - Next column is more cardinal, less important. It can be rounded time sometimes, or
site_id, orsource_id, orgroup_idor something similar. - repeat p.3 once again (or few times)
- if you added already all columns important for filtering and you still not addressing a single row with you pk - you can add more columns which can help to put similar records close to each other (to improve the compression)
- if you have something like hierarchy / tree-like relations between the columns - put there the records from ‘root’ to ’leaves’ for example (continent, country, cityname). This way clickhouse can do lookup by country / city even if continent is not specified (it will just ‘check all continents’) special variants of MergeTree may require special ORDER BY to make the record unique etc.
- For timeseries it usually make sense to put timestamp as latest column in ORDER BY, it helps with putting the same data near by for better locality. There is only 2 major patterns for timestamps in ORDER BY: (…, toStartOf(Day|Hour|…)(timestamp), …, timestamp) and (…, timestamp). First one is useful when your often query small part of table partition. (table partitioned by months and your read only 1-4 days 90% of times)
Some examples or good order by
ORDER BY (tenantid, site_id, utm_source, clientid, timestamp)
ORDER BY (site_id, toStartOfHour(timestamp), sessionid, timestamp )
PRIMARY KEY (site_id, toStartOfHour(timestamp), sessionid)
For Summing / Aggregating
All dimensions go to ORDER BY, all metrics - outside of that.
The most important for filtering columns with the lowest cardinality should be the left most.
If number of dimensions is high it’s typically make sense to use a prefix of ORDER BY as a PRIMARY KEY to avoid polluting sparse index.
Examples:
ORDER BY (tenant_id, hour, country_code, team_id, group_id, source_id)
PRIMARY KEY (tenant_id, hour, country_code, team_id)
For Replacing / Collapsing
You need to keep all ‘mutable’ columns outside of ORDER BY, and have some unique id (a base to collapse duplicates) inside. Typically the right-most column is some row identifier. And it’s often not needed in sparse index (so PRIMARY KEY can be a prefix of ORDER BY) The rest consideration are the same.
Examples:
ORDER BY (tenantid, site_id, eventid) -- utm_source is mutable, while tenantid, site_id is not
PRIMARY KEY (tenantid, site_id) -- eventid is not used for filtering, needed only for collapsing duplicates
ORDER BY example
-- col1: high Cardinality
-- col2: low cardinality
CREATE TABLE tests.order_test
(
`col1` DateTime,
`col2` UInt8
)
ENGINE = MergeTree
PARTITION BY toYYYYMM(col1)
ORDER BY (col1, col2)
--
SELECT count()
┌───count()─┐
│ 126371225 │
└───────────┘
So let’s put the highest cardinal column to the left and the least to the right in the ORDER BY definition. This will impact in queries like:
SELECT * FROM order_test
WHERE col1 > toDateTime('2020-10-01')
ORDER BY col1, col2
FORMAT `Null`
Here for the filtering it will use the skipping index to select the parts WHERE col1 > xxx and the result wont be need to be ordered because the ORDER BY in the query aligns with the ORDER BY in the table and the data is already ordered in disk.
executeQuery: (from [::ffff:192.168.11.171]:39428, user: admin) SELECT * FROM order_test WHERE col1 > toDateTime('2020-10-01') ORDER BY col1,col2 FORMAT Null; (stage: Complete)
ContextAccess (admin): Access granted: SELECT(col1, col2) ON tests.order_test
ContextAccess (admin): Access granted: SELECT(col1, col2) ON tests.order_test
InterpreterSelectQuery: FetchColumns -> Complete
tests.order_test (SelectExecutor): Key condition: (column 0 in [1601503201, +Inf))
tests.order_test (SelectExecutor): MinMax index condition: (column 0 in [1601503201, +Inf))
tests.order_test (SelectExecutor): Running binary search on index range for part 202010_367_545_8 (7612 marks)
tests.order_test (SelectExecutor): Running binary search on index range for part 202010_549_729_12 (37 marks)
tests.order_test (SelectExecutor): Running binary search on index range for part 202011_689_719_2 (1403 marks)
tests.order_test (SelectExecutor): Running binary search on index range for part 202012_550_730_12 (3 marks)
tests.order_test (SelectExecutor): Found (LEFT) boundary mark: 0
tests.order_test (SelectExecutor): Found (LEFT) boundary mark: 0
tests.order_test (SelectExecutor): Found (LEFT) boundary mark: 0
tests.order_test (SelectExecutor): Found (RIGHT) boundary mark: 37
tests.order_test (SelectExecutor): Found (RIGHT) boundary mark: 3
tests.order_test (SelectExecutor): Found (RIGHT) boundary mark: 1403
tests.order_test (SelectExecutor): Found continuous range in 11 steps
tests.order_test (SelectExecutor): Found continuous range in 3 steps
tests.order_test (SelectExecutor): Running binary search on index range for part 202011_728_728_0 (84 marks)
tests.order_test (SelectExecutor): Found continuous range in 21 steps
tests.order_test (SelectExecutor): Running binary search on index range for part 202011_725_725_0 (128 marks)
tests.order_test (SelectExecutor): Found (LEFT) boundary mark: 0
tests.order_test (SelectExecutor): Found (LEFT) boundary mark: 0
tests.order_test (SelectExecutor): Found (RIGHT) boundary mark: 84
tests.order_test (SelectExecutor): Running binary search on index range for part 202011_722_722_0 (128 marks)
tests.order_test (SelectExecutor): Found continuous range in 13 steps
tests.order_test (SelectExecutor): Found (RIGHT) boundary mark: 128
tests.order_test (SelectExecutor): Found continuous range in 14 steps
tests.order_test (SelectExecutor): Running binary search on index range for part 202011_370_686_19 (5993 marks)
tests.order_test (SelectExecutor): Found (LEFT) boundary mark: 0
tests.order_test (SelectExecutor): Found (RIGHT) boundary mark: 5993
tests.order_test (SelectExecutor): Found (LEFT) boundary mark: 0
tests.order_test (SelectExecutor): Found continuous range in 25 steps
tests.order_test (SelectExecutor): Found (RIGHT) boundary mark: 128
tests.order_test (SelectExecutor): Found continuous range in 14 steps
tests.order_test (SelectExecutor): Found (LEFT) boundary mark: 0
tests.order_test (SelectExecutor): Found (RIGHT) boundary mark: 7612
tests.order_test (SelectExecutor): Found continuous range in 25 steps
tests.order_test (SelectExecutor): Selected 8/9 parts by partition key, 8 parts by primary key, 15380/15380 marks by primary key, 15380 marks to read from 8 ranges
Ok.
0 rows in set. Elapsed: 0.649 sec. Processed 125.97 million rows, 629.86 MB (194.17 million rows/s., 970.84 MB/s.)
If we change the ORDER BY expression in the query, Clickhouse will need to retrieve the rows and reorder them:
SELECT * FROM order_test
WHERE col1 > toDateTime('2020-10-01')
ORDER BY col2, col1
FORMAT `Null`
As seen In the MergingSortedTransform message, the ORDER BY in the table definition is not aligned with the ORDER BY in the query, so ClickHouse has to reorder the resultset.
executeQuery: (from [::ffff:192.168.11.171]:39428, user: admin) SELECT * FROM order_test WHERE col1 > toDateTime('2020-10-01') ORDER BY col2,col1 FORMAT Null; (stage: Complete)
ContextAccess (admin): Access granted: SELECT(col1, col2) ON tests.order_test
ContextAccess (admin): Access granted: SELECT(col1, col2) ON tests.order_test
InterpreterSelectQuery: FetchColumns -> Complete
tests.order_test (SelectExecutor): Key condition: (column 0 in [1601503201, +Inf))
tests.order_test (SelectExecutor): MinMax index condition: (column 0 in [1601503201, +Inf))
tests.order_test (SelectExecutor): Running binary search on index range for part 202010_367_545_8 (7612 marks)
tests.order_test (SelectExecutor): Running binary search on index range for part 202012_550_730_12 (3 marks)
tests.order_test (SelectExecutor): Found (LEFT) boundary mark: 0
tests.order_test (SelectExecutor): Running binary search on index range for part 202011_725_725_0 (128 marks)
tests.order_test (SelectExecutor): Found (RIGHT) boundary mark: 3
tests.order_test (SelectExecutor): Running binary search on index range for part 202011_689_719_2 (1403 marks)
tests.order_test (SelectExecutor): Running binary search on index range for part 202010_549_729_12 (37 marks)
tests.order_test (SelectExecutor): Running binary search on index range for part 202011_728_728_0 (84 marks)
tests.order_test (SelectExecutor): Found (LEFT) boundary mark: 0
tests.order_test (SelectExecutor): Found continuous range in 3 steps
tests.order_test (SelectExecutor): Found (LEFT) boundary mark: 0
tests.order_test (SelectExecutor): Found (LEFT) boundary mark: 0
tests.order_test (SelectExecutor): Found (LEFT) boundary mark: 0
tests.order_test (SelectExecutor): Running binary search on index range for part 202011_722_722_0 (128 marks)
tests.order_test (SelectExecutor): Found (RIGHT) boundary mark: 7612
tests.order_test (SelectExecutor): Found (RIGHT) boundary mark: 37
tests.order_test (SelectExecutor): Found (LEFT) boundary mark: 0
tests.order_test (SelectExecutor): Found continuous range in 11 steps
tests.order_test (SelectExecutor): Found (RIGHT) boundary mark: 1403
tests.order_test (SelectExecutor): Found (RIGHT) boundary mark: 84
tests.order_test (SelectExecutor): Found continuous range in 25 steps
tests.order_test (SelectExecutor): Running binary search on index range for part 202011_370_686_19 (5993 marks)
tests.order_test (SelectExecutor): Found continuous range in 21 steps
tests.order_test (SelectExecutor): Found (RIGHT) boundary mark: 128
tests.order_test (SelectExecutor): Found continuous range in 13 steps
tests.order_test (SelectExecutor): Found (LEFT) boundary mark: 0
tests.order_test (SelectExecutor): Found continuous range in 14 steps
tests.order_test (SelectExecutor): Found (RIGHT) boundary mark: 128
tests.order_test (SelectExecutor): Found (LEFT) boundary mark: 0
tests.order_test (SelectExecutor): Found continuous range in 14 steps
tests.order_test (SelectExecutor): Found (RIGHT) boundary mark: 5993
tests.order_test (SelectExecutor): Found continuous range in 25 steps
tests.order_test (SelectExecutor): Selected 8/9 parts by partition key, 8 parts by primary key, 15380/15380 marks by primary key, 15380 marks to read from 8 ranges
tests.order_test (SelectExecutor): MergingSortedTransform: Merge sorted 1947 blocks, 125972070 rows in 1.423973879 sec., 88465155.05499662 rows/sec., 423.78 MiB/sec
Ok.
0 rows in set. Elapsed: 1.424 sec. Processed 125.97 million rows, 629.86 MB (88.46 million rows/s., 442.28 MB/s.)
PARTITION BY
- Good size for single partition is something like 1-300Gb.
- For Summing/Replacing a bit smaller (400Mb-40Gb)
- Better to avoid touching more that few dozens of partitions with typical SELECT query.
- Single insert should bring data to one or few partitions.
- The number of partitons in table - dozen or hundreds, not thousands.
The size of partitions you can check in system.parts table.
Examples:
-- for time-series:
PARTITION BY toYear(timestamp) -- long retention, not too much data
PARTITION BY toYYYYMM(timestamp) --
PARTITION BY toMonday(timestamp) --
PARTITION BY toDate(timestamp) --
PARTITION BY toStartOfHour(timestamp) -- short retention, lot of data
-- for table with some incremental (non time-bounded) counter
PARTITION BY intDiv(transaction_id, 1000000)
-- for some dimention tables (always requested with WHERE userid)
PARTITION BY userid % 16
For the small tables (smaller than few gigabytes) partitioning is usually not needed at all (just skip PARTITION BY expresssion when you create the table).
ClickHouse Anti-Patterns. Learning from Users’ Mistakes
A short talk by Mikhail Filimonov
4 - AggregatingMergeTree
AggregatingMergeTree
Q. What happens with columns which are nor the part of ORDER BY key, nor have the AggregateFunction type?
A. it picks the first value met, (similar to any)
CREATE TABLE agg_test
(
`a` String,
`b` UInt8,
`c` SimpleAggregateFunction(max, UInt8)
)
ENGINE = AggregatingMergeTree
ORDER BY a;
INSERT INTO agg_test VALUES ('a', 1, 1);
INSERT INTO agg_test VALUES ('a', 2, 2);
SELECT * FROM agg_test FINAL;
┌─a─┬─b─┬─c─┐
│ a │ 1 │ 2 │
└───┴───┴───┘
INSERT INTO agg_test VALUES ('a', 3, 3);
SELECT * FROM agg_test;
┌─a─┬─b─┬─c─┐
│ a │ 1 │ 2 │
└───┴───┴───┘
┌─a─┬─b─┬─c─┐
│ a │ 3 │ 3 │
└───┴───┴───┘
OPTIMIZE TABLE agg_test FINAL;
SELECT * FROM agg_test;
┌─a─┬─b─┬─c─┐
│ a │ 1 │ 3 │
└───┴───┴───┘
Last non-null value for each column
CREATE TABLE test_last
(
`col1` Int32,
`col2` SimpleAggregateFunction(anyLast, Nullable(DateTime)),
`col3` SimpleAggregateFunction(anyLast, Nullable(DateTime))
)
ENGINE = AggregatingMergeTree
ORDER BY col1
Ok.
0 rows in set. Elapsed: 0.003 sec.
INSERT INTO test_last (col1, col2) VALUES (1, now());
Ok.
1 rows in set. Elapsed: 0.014 sec.
INSERT INTO test_last (col1, col3) VALUES (1, now())
Ok.
1 rows in set. Elapsed: 0.006 sec.
SELECT
col1,
anyLast(col2),
anyLast(col3)
FROM test_last
GROUP BY col1
┌─col1─┬───────anyLast(col2)─┬───────anyLast(col3)─┐
│ 1 │ 2020-01-16 20:57:46 │ 2020-01-16 20:57:51 │
└──────┴─────────────────────┴─────────────────────┘
1 rows in set. Elapsed: 0.005 sec.
SELECT *
FROM test_last
FINAL
┌─col1─┬────────────────col2─┬────────────────col3─┐
│ 1 │ 2020-01-16 20:57:46 │ 2020-01-16 20:57:51 │
└──────┴─────────────────────┴─────────────────────┘
1 rows in set. Elapsed: 0.003 sec.
5 - index & column files
index & column files
6 - Merge performance and OPTIMIZE FINAL
Merge performance and OPTIMIZE FINAL DEDUPLICATE BY expr
Merge Performance
Main things affecting the merge speed are:
- Schema (especially compression codecs, some bad types, sorting order…)
- Horizontal vs Vertical merge
- Horizontal = reads all columns at once, do merge sort, write new part
- Vertical = first read columns from order by, do merge sort, write them to disk, remember permutation, then process the rest of columns on by one, applying permutation.
- compact vs wide parts
- Other things like server load, concurrent merges…
SELECT name, value
FROM system.merge_tree_settings
WHERE name LIKE '%vert%';
│ enable_vertical_merge_algorithm │ 1
│ vertical_merge_algorithm_min_rows_to_activate │ 131072
│ vertical_merge_algorithm_min_columns_to_activate │ 11
- Vertical merge will be used if part has more than 131072 rows and more than 11 columns in the table.
-- Disable Vertical Merges
ALTER TABLE test MODIFY SETTING enable_vertical_merge_algorithm = 0
- Horizontal merge used by default, will use more memory if there are more than 80 columns in the table
OPTIMIZE TABLE example FINAL DEDUPLICATE BY expr
When using deduplicate feature in OPTIMIZE FINAL, the question is which row will remain and won’t be deduped?
For SELECT operations Clickhouse does not guarantee the order of the resultset unless you specify ORDER BY. This random ordering is affected by different parameters, like for example max_threads.
In a merge operation ClickHouse reads rows sequentially in storage order, which is determined by ORDER BY specified in CREATE TABLE statement, and only the first unique row in that order survives deduplication. So it is a bit different from how SELECT actually works. As FINAL clause is used then ClickHouse will merge all rows across all partitions (If it is not specified then the merge operation will be done per partition), and so the first unique row of the first partition will survive deduplication. Merges are single-threaded because it is too complicated to apply merge ops in-parallel, and it generally makes no sense.
7 - Nulls in order by
Nulls in order by
- It is NOT RECOMMENDED for a general use
- Use on your own risk
- Use latest ClickHouse version if you need that.
CREATE TABLE x
(
`a` Nullable(UInt32),
`b` Nullable(UInt32),
`cnt` UInt32
)
ENGINE = SummingMergeTree
ORDER BY (a, b)
SETTINGS allow_nullable_key = 1;
INSERT INTO x VALUES (Null,2,1), (Null,Null,1), (3, Null, 1), (4,4,1);
INSERT INTO x VALUES (Null,2,1), (Null,Null,1), (3, Null, 1), (4,4,1);
SELECT * FROM x;
┌────a─┬────b─┬─cnt─┐
│ 3 │ null │ 2 │
│ 4 │ 4 │ 2 │
│ null │ 2 │ 2 │
│ null │ null │ 2 │
└──────┴──────┴─────┘
8 - ReplacingMergeTree
ReplacingMergeTree
ReplacingMergeTree is a powerful ClickHouse MergeTree engine. It is one of the techniques that can be used to guarantee unicity or exactly once delivery in ClickHouse.
General Operations
Engine Parameters
Engine = ReplacingMergeTree([version_column],[is_deleted_column])
ORDER BY <list_of_columns>
- ORDER BY – The ORDER BY defines the columns that need to be unique at merge time. Since merge time can not be decided most of the time, the FINAL keyword is required to remove duplicates.
- version_column – An monotonically increasing number, which can be based on a timestamp. Used for make sure sure updates are executed in a right order.
- is_deleted_column (23.2+ see https://github.com/ClickHouse/ClickHouse/pull/41005) – the column used to delete rows.
DML operations
- CREATE –
INSERT INTO t values(..) - READ –
SELECT FROM t final - UPDATE –
INSERT INTO t(..., _version) values (...), insert with incremented version - DELETE –
INSERT INTO t(..., _version, is_deleted) values(..., 1)
FINAL
ClickHouse does not guarantee that merge will fire and replace rows using ReplacingMergeTree logic. FINAL keyword should be used in order to apply merge in a query time. It works reasonably fast when PK filter is used, but maybe slow for SELECT * type of queries:
See those links for reference:
Since 23.2, profile level final=1 can force final automatically, see https://github.com/ClickHouse/ClickHouse/pull/40945
Clickhouse merge parts only in scope of single partition, so if two rows with the same replacing key would land in different partitions, they would never be merged in single row. FINAL keyword works in other way, it merge all rows across all partitions. But that behavior can be changed viado_not_merge_across_partitions_select_final setting.
CREATE TABLE repl_tbl_part
(
`key` UInt32,
`value` UInt32,
`part_key` UInt32
)
ENGINE = ReplacingMergeTree
PARTITION BY part_key
ORDER BY key;
INSERT INTO repl_tbl_part SELECT
1 AS key,
number AS value,
number % 2 AS part_key
FROM numbers(4)
SETTINGS optimize_on_insert = 0;
SELECT * FROM repl_tbl_part;
┌─key─┬─value─┬─part_key─┐
│ 1 │ 1 │ 1 │
│ 1 │ 3 │ 1 │
└─────┴───────┴──────────┘
┌─key─┬─value─┬─part_key─┐
│ 1 │ 0 │ 0 │
│ 1 │ 2 │ 0 │
└─────┴───────┴──────────┘
SELECT * FROM repl_tbl_part FINAL;
┌─key─┬─value─┬─part_key─┐
│ 1 │ 3 │ 1 │
└─────┴───────┴──────────┘
SELECT * FROM repl_tbl_part FINAL SETTINGS do_not_merge_across_partitions_select_final=1;
┌─key─┬─value─┬─part_key─┐
│ 1 │ 3 │ 1 │
└─────┴───────┴──────────┘
┌─key─┬─value─┬─part_key─┐
│ 1 │ 2 │ 0 │
└─────┴───────┴──────────┘
OPTIMIZE TABLE repl_tbl_part FINAL;
SELECT * FROM repl_tbl_part;
┌─key─┬─value─┬─part_key─┐
│ 1 │ 3 │ 1 │
└─────┴───────┴──────────┘
┌─key─┬─value─┬─part_key─┐
│ 1 │ 2 │ 0 │
└─────┴───────┴──────────┘
Deleting the data
- Delete in partition:
ALTER TABLE t DELETE WHERE ... in PARTITION 'partition'– slow and asynchronous, rebuilds the partition - Filter is_deleted in queries:
SELECT ... WHERE is_deleted = 0 - Before 23.2, use ROW POLICY to apply a filter automatically:
CREATE ROW POLICY delete_masking on t using is_deleted = 0 for ALL; - 23.2+
ReplacingMergeTree(version, is_deleted) ORDER BY .. SETTINGS clean_deleted_rows='Always'(see https://github.com/ClickHouse/ClickHouse/pull/41005)
Other options:
- Partition operations:
ALTER TABLE t DROP PARTITION 'partition'– locks the table, drops full partition only - Lightwieght delete:
DELETE FROM t WHERE ...– experimental
Use cases
Last state
CREATE TABLE repl_tbl
(
`key` UInt32,
`val_1` UInt32,
`val_2` String,
`val_3` String,
`val_4` String,
`val_5` UUID,
`ts` DateTime
)
ENGINE = ReplacingMergeTree(ts)
ORDER BY key
SYSTEM STOP MERGES repl_tbl;
INSERT INTO repl_tbl SELECT number as key, rand() as val_1, randomStringUTF8(10) as val_2, randomStringUTF8(5) as val_3, randomStringUTF8(4) as val_4, generateUUIDv4() as val_5, now() as ts FROM numbers(10000000);
INSERT INTO repl_tbl SELECT number as key, rand() as val_1, randomStringUTF8(10) as val_2, randomStringUTF8(5) as val_3, randomStringUTF8(4) as val_4, generateUUIDv4() as val_5, now() as ts FROM numbers(10000000);
INSERT INTO repl_tbl SELECT number as key, rand() as val_1, randomStringUTF8(10) as val_2, randomStringUTF8(5) as val_3, randomStringUTF8(4) as val_4, generateUUIDv4() as val_5, now() as ts FROM numbers(10000000);
INSERT INTO repl_tbl SELECT number as key, rand() as val_1, randomStringUTF8(10) as val_2, randomStringUTF8(5) as val_3, randomStringUTF8(4) as val_4, generateUUIDv4() as val_5, now() as ts FROM numbers(10000000);
SELECT count() FROM repl_tbl
┌──count()─┐
│ 50000000 │
└──────────┘
Single key
-- GROUP BY
SELECT key, argMax(val_1, ts) as val_1, argMax(val_2, ts) as val_2, argMax(val_3, ts) as val_3, argMax(val_4, ts) as val_4, argMax(val_5, ts) as val_5, max(ts) FROM repl_tbl WHERE key = 10 GROUP BY key;
1 rows in set. Elapsed: 0.017 sec. Processed 40.96 thousand rows, 5.24 MB (2.44 million rows/s., 312.31 MB/s.)
-- ORDER BY LIMIT BY
SELECT * FROM repl_tbl WHERE key = 10 ORDER BY ts DESC LIMIT 1 BY key ;
1 rows in set. Elapsed: 0.017 sec. Processed 40.96 thousand rows, 5.24 MB (2.39 million rows/s., 305.41 MB/s.)
-- Subquery
SELECT * FROM repl_tbl WHERE key = 10 AND ts = (SELECT max(ts) FROM repl_tbl WHERE key = 10);
1 rows in set. Elapsed: 0.019 sec. Processed 40.96 thousand rows, 1.18 MB (2.20 million rows/s., 63.47 MB/s.)
-- FINAL
SELECT * FROM repl_tbl FINAL WHERE key = 10;
1 rows in set. Elapsed: 0.021 sec. Processed 40.96 thousand rows, 5.24 MB (1.93 million rows/s., 247.63 MB/s.)
Multiple keys
-- GROUP BY
SELECT key, argMax(val_1, ts) as val_1, argMax(val_2, ts) as val_2, argMax(val_3, ts) as val_3, argMax(val_4, ts) as val_4, argMax(val_5, ts) as val_5, max(ts) FROM repl_tbl WHERE key IN (SELECT toUInt32(number) FROM numbers(1000000) WHERE number % 100) GROUP BY key FORMAT Null;
Peak memory usage (for query): 2.31 GiB.
0 rows in set. Elapsed: 3.264 sec. Processed 5.04 million rows, 645.01 MB (1.54 million rows/s., 197.60 MB/s.)
-- set optimize_aggregation_in_order=1;
Peak memory usage (for query): 1.11 GiB.
0 rows in set. Elapsed: 1.772 sec. Processed 2.74 million rows, 350.30 MB (1.54 million rows/s., 197.73 MB/s.)
-- ORDER BY LIMIT BY
SELECT * FROM repl_tbl WHERE key IN (SELECT toUInt32(number) FROM numbers(1000000) WHERE number % 100) ORDER BY ts DESC LIMIT 1 BY key FORMAT Null;
Peak memory usage (for query): 1.08 GiB.
0 rows in set. Elapsed: 2.429 sec. Processed 5.04 million rows, 645.01 MB (2.07 million rows/s., 265.58 MB/s.)
-- Subquery
SELECT * FROM repl_tbl WHERE (key, ts) IN (SELECT key, max(ts) FROM repl_tbl WHERE key IN (SELECT toUInt32(number) FROM numbers(1000000) WHERE number % 100) GROUP BY key) FORMAT Null;
Peak memory usage (for query): 432.57 MiB.
0 rows in set. Elapsed: 0.939 sec. Processed 5.04 million rows, 160.33 MB (5.36 million rows/s., 170.69 MB/s.)
-- set optimize_aggregation_in_order=1;
Peak memory usage (for query): 202.88 MiB.
0 rows in set. Elapsed: 0.824 sec. Processed 5.04 million rows, 160.33 MB (6.11 million rows/s., 194.58 MB/s.)
-- FINAL
SELECT * FROM repl_tbl FINAL WHERE key IN (SELECT toUInt32(number) FROM numbers(1000000) WHERE number % 100) FORMAT Null;
Peak memory usage (for query): 198.32 MiB.
0 rows in set. Elapsed: 1.211 sec. Processed 5.04 million rows, 645.01 MB (4.16 million rows/s., 532.57 MB/s.)
Full table
-- GROUP BY
SELECT key, argMax(val_1, ts) as val_1, argMax(val_2, ts) as val_2, argMax(val_3, ts) as val_3, argMax(val_4, ts) as val_4, argMax(val_5, ts) as val_5, max(ts) FROM repl_tbl GROUP BY key FORMAT Null;
Peak memory usage (for query): 15.02 GiB.
0 rows in set. Elapsed: 19.164 sec. Processed 50.00 million rows, 6.40 GB (2.61 million rows/s., 334.02 MB/s.)
-- set optimize_aggregation_in_order=1;
Peak memory usage (for query): 4.44 GiB.
0 rows in set. Elapsed: 9.700 sec. Processed 21.03 million rows, 2.69 GB (2.17 million rows/s., 277.50 MB/s.)
-- ORDER BY LIMIT BY
SELECT * FROM repl_tbl ORDER BY ts DESC LIMIT 1 BY key FORMAT Null;
Peak memory usage (for query): 10.46 GiB.
0 rows in set. Elapsed: 21.264 sec. Processed 50.00 million rows, 6.40 GB (2.35 million rows/s., 301.03 MB/s.)
-- Subquery
SELECT * FROM repl_tbl WHERE (key, ts) IN (SELECT key, max(ts) FROM repl_tbl GROUP BY key) FORMAT Null;
Peak memory usage (for query): 2.52 GiB.
0 rows in set. Elapsed: 6.891 sec. Processed 50.00 million rows, 1.60 GB (7.26 million rows/s., 232.22 MB/s.)
-- set optimize_aggregation_in_order=1;
Peak memory usage (for query): 1.05 GiB.
0 rows in set. Elapsed: 4.427 sec. Processed 50.00 million rows, 1.60 GB (11.29 million rows/s., 361.49 MB/s.)
-- FINAL
SELECT * FROM repl_tbl FINAL FORMAT Null;
Peak memory usage (for query): 838.75 MiB.
0 rows in set. Elapsed: 6.681 sec. Processed 50.00 million rows, 6.40 GB (7.48 million rows/s., 958.18 MB/s.)
8.1 - ReplacingMergeTree does not collapse duplicates
ReplacingMergeTree does not collapse duplicates
Hi there, I have a question about replacing merge trees. I have set up a Materialized View with ReplacingMergeTree table, but even if I call optimize on it, the parts don’t get merged. I filled that table yesterday, nothing happened since then. What should I do?
Merges are eventual and may never happen. It depends on the number of inserts that happened after, the number of parts in the partition, size of parts. If the total size of input parts are greater than the maximum part size then they will never be merged.
https://clickhouse.tech/docs/en/engines/table-engines/mergetree-family/replacingmergetree/ ReplacingMergeTree is suitable for clearing out duplicate data in the background in order to save space, but it doesn’t guarantee the absence of duplicates.
9 - Skip index
Skip index
Warning
When you are creating skip indexes in non-regular (Replicated)MergeTree tables over non ORDER BY columns. ClickHouse applies index condition on the first step of query execution, so it’s possible to get outdated rows.--(1) create test table
drop table if exists test;
create table test
(
version UInt32
,id UInt32
,state UInt8
,INDEX state_idx (state) type set(0) GRANULARITY 1
) ENGINE ReplacingMergeTree(version)
ORDER BY (id);
--(2) insert sample data
INSERT INTO test (version, id, state) VALUES (1,1,1);
INSERT INTO test (version, id, state) VALUES (2,1,0);
INSERT INTO test (version, id, state) VALUES (3,1,1);
--(3) check the result:
-- expected 3, 1, 1
select version, id, state from test final;
┌─version─┬─id─┬─state─┐
│ 3 │ 1 │ 1 │
└─────────┴────┴───────┘
-- expected empty result
select version, id, state from test final where state=0;
┌─version─┬─id─┬─state─┐
│ 2 │ 1 │ 0 │
└─────────┴────┴───────┘
10 - SummingMergeTree
SummingMergeTree
Nested structures
In certain conditions it could make sense to collapse one of dimensions to set of arrays. It’s usually profitable to do if this dimension is not commonly used in queries. It would reduce amount of rows in aggregated table and speed up queries which doesn’t care about this dimension in exchange of aggregation performance by collapsed dimension.
CREATE TABLE traffic
(
`key1` UInt32,
`key2` UInt32,
`port` UInt16,
`bits_in` UInt32 CODEC (T64,LZ4),
`bits_out` UInt32 CODEC (T64,LZ4),
`packets_in` UInt32 CODEC (T64,LZ4),
`packets_out` UInt32 CODEC (T64,LZ4)
)
ENGINE = SummingMergeTree
ORDER BY (key1, key2, port);
INSERT INTO traffic SELECT
number % 1000,
intDiv(number, 10000),
rand() % 20,
rand() % 753,
rand64() % 800,
rand() % 140,
rand64() % 231
FROM numbers(100000000);
CREATE TABLE default.traffic_map
(
`key1` UInt32,
`key2` UInt32,
`bits_in` UInt32 CODEC(T64, LZ4),
`bits_out` UInt32 CODEC(T64, LZ4),
`packets_in` UInt32 CODEC(T64, LZ4),
`packets_out` UInt32 CODEC(T64, LZ4),
`portMap.port` Array(UInt16),
`portMap.bits_in` Array(UInt32) CODEC(T64, LZ4),
`portMap.bits_out` Array(UInt32) CODEC(T64, LZ4),
`portMap.packets_in` Array(UInt32) CODEC(T64, LZ4),
`portMap.packets_out` Array(UInt32) CODEC(T64, LZ4)
)
ENGINE = SummingMergeTree
ORDER BY (key1, key2);
INSERT INTO traffic_map WITH rand() % 20 AS port
SELECT
number % 1000 AS key1,
intDiv(number, 10000) AS key2,
rand() % 753 AS bits_in,
rand64() % 800 AS bits_out,
rand() % 140 AS packets_in,
rand64() % 231 AS packets_out,
[port],
[bits_in],
[bits_out],
[packets_in],
[packets_out]
FROM numbers(100000000);
┌─table───────┬─column──────────────┬─────rows─┬─compressed─┬─uncompressed─┬──ratio─┐
│ traffic │ bits_out │ 80252317 │ 109.09 MiB │ 306.14 MiB │ 2.81 │
│ traffic │ bits_in │ 80252317 │ 108.34 MiB │ 306.14 MiB │ 2.83 │
│ traffic │ port │ 80252317 │ 99.21 MiB │ 153.07 MiB │ 1.54 │
│ traffic │ packets_out │ 80252317 │ 91.36 MiB │ 306.14 MiB │ 3.35 │
│ traffic │ packets_in │ 80252317 │ 84.61 MiB │ 306.14 MiB │ 3.62 │
│ traffic │ key2 │ 80252317 │ 47.88 MiB │ 306.14 MiB │ 6.39 │
│ traffic │ key1 │ 80252317 │ 1.38 MiB │ 306.14 MiB │ 221.42 │
│ traffic_map │ portMap.bits_out │ 10000000 │ 108.96 MiB │ 306.13 MiB │ 2.81 │
│ traffic_map │ portMap.bits_in │ 10000000 │ 108.32 MiB │ 306.13 MiB │ 2.83 │
│ traffic_map │ portMap.port │ 10000000 │ 92.00 MiB │ 229.36 MiB │ 2.49 │
│ traffic_map │ portMap.packets_out │ 10000000 │ 90.95 MiB │ 306.13 MiB │ 3.37 │
│ traffic_map │ portMap.packets_in │ 10000000 │ 84.19 MiB │ 306.13 MiB │ 3.64 │
│ traffic_map │ key2 │ 10000000 │ 23.46 MiB │ 38.15 MiB │ 1.63 │
│ traffic_map │ bits_in │ 10000000 │ 15.59 MiB │ 38.15 MiB │ 2.45 │
│ traffic_map │ bits_out │ 10000000 │ 15.59 MiB │ 38.15 MiB │ 2.45 │
│ traffic_map │ packets_out │ 10000000 │ 13.22 MiB │ 38.15 MiB │ 2.89 │
│ traffic_map │ packets_in │ 10000000 │ 12.62 MiB │ 38.15 MiB │ 3.02 │
│ traffic_map │ key1 │ 10000000 │ 180.29 KiB │ 38.15 MiB │ 216.66 │
└─────────────┴─────────────────────┴──────────┴────────────┴──────────────┴────────┘
-- Queries
SELECT
key1,
sum(packets_in),
sum(bits_out)
FROM traffic
GROUP BY key1
FORMAT `Null`
0 rows in set. Elapsed: 0.488 sec. Processed 80.25 million rows, 963.03 MB (164.31 million rows/s., 1.97 GB/s.)
SELECT
key1,
sum(packets_in),
sum(bits_out)
FROM traffic_map
GROUP BY key1
FORMAT `Null`
0 rows in set. Elapsed: 0.063 sec. Processed 10.00 million rows, 120.00 MB (159.43 million rows/s., 1.91 GB/s.)
SELECT
key1,
port,
sum(packets_in),
sum(bits_out)
FROM traffic
GROUP BY
key1,
port
FORMAT `Null`
0 rows in set. Elapsed: 0.668 sec. Processed 80.25 million rows, 1.12 GB (120.14 million rows/s., 1.68 GB/s.)
WITH arrayJoin(arrayZip(untuple(sumMap(portMap.port, portMap.packets_in, portMap.bits_out)))) AS tpl
SELECT
key1,
tpl.1 AS port,
tpl.2 AS packets_in,
tpl.3 AS bits_out
FROM traffic_map
GROUP BY key1
FORMAT `Null`
0 rows in set. Elapsed: 0.915 sec. Processed 10.00 million rows, 1.08 GB (10.93 million rows/s., 1.18 GB/s.)