ClickHouse/docs/en/sql-reference/dictionaries/external-dictionaries/external-dicts-dict-layout.md

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/en/sql-reference/dictionaries/external-dictionaries/external-dicts-dict-layout	41	Storing Dictionaries in Memory

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Storing Dictionaries in Memory

There are a variety of ways to store dictionaries in memory.

We recommend flat, hashed and complex_key_hashed, which provide optimal processing speed.

Caching is not recommended because of potentially poor performance and difficulties in selecting optimal parameters. Read more in the section cache.

There are several ways to improve dictionary performance:

• Call the function for working with the dictionary after GROUP BY.
• Mark attributes to extract as injective. An attribute is called injective if different attribute values correspond to different keys. So when GROUP BY uses a function that fetches an attribute value by the key, this function is automatically taken out of GROUP BY.

ClickHouse generates an exception for errors with dictionaries. Examples of errors:

• The dictionary being accessed could not be loaded.
• Error querying a cached dictionary.

You can view the list of dictionaries and their statuses in the system.dictionaries table.

The configuration looks like this:

<clickhouse>
    <dictionary>
        ...
        <layout>
            <layout_type>
                <!-- layout settings -->
            </layout_type>
        </layout>
        ...
    </dictionary>
</clickhouse>

Corresponding DDL-query:

CREATE DICTIONARY (...)
...
LAYOUT(LAYOUT_TYPE(param value)) -- layout settings
...

Dictionaries without word complex-key* in a layout have a key with UInt64 type, complex-key* dictionaries have a composite key (complex, with arbitrary types).

UInt64 keys in XML dictionaries are defined with <id> tag.

Configuration example (column key_column has UInt64 type):

...
<structure>
    <id>
        <name>key_column</name>
    </id>
...

Composite complex keys XML dictionaries are defined <key> tag.

Configuration example of a composite key (key has one element with String type):

...
<structure>
    <key>
        <attribute>
            <name>country_code</name>
            <type>String</type>
        </attribute>
    </key>
...

Ways to Store Dictionaries in Memory

• flat
• hashed
• sparse_hashed
• complex_key_hashed
• complex_key_sparse_hashed
• hashed_array
• complex_key_hashed_array
• range_hashed
• complex_key_range_hashed
• cache
• complex_key_cache
• ssd_cache
• complex_key_ssd_cache
• direct
• complex_key_direct
• ip_trie

flat

The dictionary is completely stored in memory in the form of flat arrays. How much memory does the dictionary use? The amount is proportional to the size of the largest key (in space used).

The dictionary key has the UInt64 type and the value is limited to max_array_size (by default — 500,000). If a larger key is discovered when creating the dictionary, ClickHouse throws an exception and does not create the dictionary. Dictionary flat arrays initial size is controlled by initial_array_size setting (by default — 1024).

All types of sources are supported. When updating, data (from a file or from a table) is read in it entirety.

This method provides the best performance among all available methods of storing the dictionary.

Configuration example:

<layout>
  <flat>
    <initial_array_size>50000</initial_array_size>
    <max_array_size>5000000</max_array_size>
  </flat>
</layout>

or

LAYOUT(FLAT(INITIAL_ARRAY_SIZE 50000 MAX_ARRAY_SIZE 5000000))

hashed

The dictionary is completely stored in memory in the form of a hash table. The dictionary can contain any number of elements with any identifiers In practice, the number of keys can reach tens of millions of items.

The dictionary key has the UInt64 type.

All types of sources are supported. When updating, data (from a file or from a table) is read in its entirety.

Configuration example:

<layout>
  <hashed />
</layout>

or

LAYOUT(HASHED())

If shards greater then 1 (default is 1) the dictionary will load data in parallel, useful if you have huge amount of elements in one dictionary.

Configuration example:

<layout>
  <hashed>
    <shards>10</shards>
    <!-- Size of the backlog for blocks in parallel queue.

         Since the bottleneck in parallel loading is rehash, and so to avoid
         stalling because of thread is doing rehash, you need to have some
         backlog.

         10000 is good balance between memory and speed.
         Even for 10e10 elements and can handle all the load without starvation. -->
    <shard_load_queue_backlog>10000</shard_load_queue_backlog>
  </hashed>
</layout>

or

LAYOUT(HASHED(SHARDS 10 [SHARD_LOAD_QUEUE_BACKLOG 10000]))

sparse_hashed

Similar to hashed, but uses less memory in favor more CPU usage.

The dictionary key has the UInt64 type.

Configuration example:

<layout>
  <sparse_hashed />
</layout>

or

LAYOUT(SPARSE_HASHED())

It is also possible to use shards for this type of dictionary, and again it is more important for sparse_hashed then for hashed, since sparse_hashed is slower.

complex_key_hashed

This type of storage is for use with composite keys. Similar to hashed.

Configuration example:

<layout>
  <complex_key_hashed>
    <shards>1</shards>
    <!-- <shard_load_queue_backlog>10000</shard_load_queue_backlog> -->
  </complex_key_hashed>
</layout>

or

LAYOUT(COMPLEX_KEY_HASHED([SHARDS 1] [SHARD_LOAD_QUEUE_BACKLOG 10000]))

complex_key_sparse_hashed

This type of storage is for use with composite keys. Similar to sparse_hashed.

Configuration example:

<layout>
  <complex_key_sparse_hashed>
    <shards>1</shards>
  </complex_key_sparse_hashed>
</layout>

or

LAYOUT(COMPLEX_KEY_SPARSE_HASHED([SHARDS 1] [SHARD_LOAD_QUEUE_BACKLOG 10000]))

hashed_array

The dictionary is completely stored in memory. Each attribute is stored in an array. The key attribute is stored in the form of a hashed table where value is an index in the attributes array. The dictionary can contain any number of elements with any identifiers. In practice, the number of keys can reach tens of millions of items.

The dictionary key has the UInt64 type.

All types of sources are supported. When updating, data (from a file or from a table) is read in its entirety.

Configuration example:

<layout>
  <hashed_array>
  </hashed_array>
</layout>

or

LAYOUT(HASHED_ARRAY())

complex_key_hashed_array

This type of storage is for use with composite keys. Similar to hashed_array.

Configuration example:

<layout>
  <complex_key_hashed_array />
</layout>

or

LAYOUT(COMPLEX_KEY_HASHED_ARRAY())

range_hashed

The dictionary is stored in memory in the form of a hash table with an ordered array of ranges and their corresponding values.

The dictionary key has the UInt64 type. This storage method works the same way as hashed and allows using date/time (arbitrary numeric type) ranges in addition to the key.

Example: The table contains discounts for each advertiser in the format:

┌─advertiser_id─┬─discount_start_date─┬─discount_end_date─┬─amount─┐
│           123 │          2015-01-16 │        2015-01-31 │   0.25 │
│           123 │          2015-01-01 │        2015-01-15 │   0.15 │
│           456 │          2015-01-01 │        2015-01-15 │   0.05 │
└───────────────┴─────────────────────┴───────────────────┴────────┘

To use a sample for date ranges, define the range_min and range_max elements in the structure. These elements must contain elements name and type (if type is not specified, the default type will be used - Date). type can be any numeric type (Date / DateTime / UInt64 / Int32 / others).

:::warning Values of range_min and range_max should fit in Int64 type. :::

Example:

<layout>
    <range_hashed>
        <!-- Strategy for overlapping ranges (min/max). Default: min (return a matching range with the min(range_min -> range_max) value) -->
        <range_lookup_strategy>min</range_lookup_strategy>
    </range_hashed>
</layout>
<structure>
    <id>
        <name>advertiser_id</name>
    </id>
    <range_min>
        <name>discount_start_date</name>
        <type>Date</type>
    </range_min>
    <range_max>
        <name>discount_end_date</name>
        <type>Date</type>
    </range_max>
    ...

or

CREATE DICTIONARY discounts_dict (
    advertiser_id UInt64,
    discount_start_date Date,
    discount_end_date Date,
    amount Float64
)
PRIMARY KEY id
SOURCE(CLICKHOUSE(TABLE 'discounts'))
LIFETIME(MIN 1 MAX 1000)
LAYOUT(RANGE_HASHED(range_lookup_strategy 'max'))
RANGE(MIN discount_start_date MAX discount_end_date)

To work with these dictionaries, you need to pass an additional argument to the dictGet function, for which a range is selected:

dictGet('dict_name', 'attr_name', id, date)

Query example:

SELECT dictGet('discounts_dict', 'amount', 1, '2022-10-20'::Date);

This function returns the value for the specified ids and the date range that includes the passed date.

Details of the algorithm:

• If the id is not found or a range is not found for the id, it returns the default value of the attribute's type.
• If there are overlapping ranges and range_lookup_strategy=min, it returns a matching range with minimal range_min, if several ranges found, it returns a range with minimal range_max, if again several ranges found (several ranges had the same range_min and range_max it returns a random range of them.
• If there are overlapping ranges and range_lookup_strategy=max, it returns a matching range with maximal range_min, if several ranges found, it returns a range with maximal range_max, if again several ranges found (several ranges had the same range_min and range_max it returns a random range of them.
• If the range_max is NULL, the range is open. NULL is treated as maximal possible value. For the range_min 1970-01-01 or 0 (-MAX_INT) can be used as the open value.

Configuration example:

<clickhouse>
    <dictionary>
        ...

        <layout>
            <range_hashed />
        </layout>

        <structure>
            <id>
                <name>Abcdef</name>
            </id>
            <range_min>
                <name>StartTimeStamp</name>
                <type>UInt64</type>
            </range_min>
            <range_max>
                <name>EndTimeStamp</name>
                <type>UInt64</type>
            </range_max>
            <attribute>
                <name>XXXType</name>
                <type>String</type>
                <null_value />
            </attribute>
        </structure>

    </dictionary>
</clickhouse>

or

CREATE DICTIONARY somedict(
    Abcdef UInt64,
    StartTimeStamp UInt64,
    EndTimeStamp UInt64,
    XXXType String DEFAULT ''
)
PRIMARY KEY Abcdef
RANGE(MIN StartTimeStamp MAX EndTimeStamp)

Configuration example with overlapping ranges and open ranges:

CREATE TABLE discounts
(
    advertiser_id UInt64,
    discount_start_date Date,
    discount_end_date Nullable(Date),
    amount Float64
)
ENGINE = Memory;

INSERT INTO discounts VALUES (1, '2015-01-01', Null, 0.1);
INSERT INTO discounts VALUES (1, '2015-01-15', Null, 0.2);
INSERT INTO discounts VALUES (2, '2015-01-01', '2015-01-15', 0.3);
INSERT INTO discounts VALUES (2, '2015-01-04', '2015-01-10', 0.4);
INSERT INTO discounts VALUES (3, '1970-01-01', '2015-01-15', 0.5);
INSERT INTO discounts VALUES (3, '1970-01-01', '2015-01-10', 0.6);

SELECT * FROM discounts ORDER BY advertiser_id, discount_start_date;
┌─advertiser_id─┬─discount_start_date─┬─discount_end_date─┬─amount─┐
│             1 │          2015-01-01 │              ᴺᵁᴸᴸ │    0.1 │
│             1 │          2015-01-15 │              ᴺᵁᴸᴸ │    0.2 │
│             2 │          2015-01-01 │        2015-01-15 │    0.3 │
│             2 │          2015-01-04 │        2015-01-10 │    0.4 │
│             3 │          1970-01-01 │        2015-01-15 │    0.5 │
│             3 │          1970-01-01 │        2015-01-10 │    0.6 │
└───────────────┴─────────────────────┴───────────────────┴────────┘

-- RANGE_LOOKUP_STRATEGY 'max'

CREATE DICTIONARY discounts_dict
(
    advertiser_id UInt64,
    discount_start_date Date,
    discount_end_date Nullable(Date),
    amount Float64
)
PRIMARY KEY advertiser_id
SOURCE(CLICKHOUSE(TABLE discounts))
LIFETIME(MIN 600 MAX 900)
LAYOUT(RANGE_HASHED(RANGE_LOOKUP_STRATEGY 'max'))
RANGE(MIN discount_start_date MAX discount_end_date);

select dictGet('discounts_dict', 'amount', 1, toDate('2015-01-14')) res;
┌─res─┐
│ 0.1 │ -- the only one range is matching: 2015-01-01 - Null
└─────┘

select dictGet('discounts_dict', 'amount', 1, toDate('2015-01-16')) res;
┌─res─┐
│ 0.2 │ -- two ranges are matching, range_min 2015-01-15 (0.2) is bigger than 2015-01-01 (0.1)
└─────┘

select dictGet('discounts_dict', 'amount', 2, toDate('2015-01-06')) res;
┌─res─┐
│ 0.4 │ -- two ranges are matching, range_min 2015-01-04 (0.4) is bigger than 2015-01-01 (0.3)
└─────┘

select dictGet('discounts_dict', 'amount', 3, toDate('2015-01-01')) res;
┌─res─┐
│ 0.5 │ -- two ranges are matching, range_min are equal, 2015-01-15 (0.5) is bigger than 2015-01-10 (0.6)
└─────┘

DROP DICTIONARY discounts_dict;

-- RANGE_LOOKUP_STRATEGY 'min'

CREATE DICTIONARY discounts_dict
(
    advertiser_id UInt64,
    discount_start_date Date,
    discount_end_date Nullable(Date),
    amount Float64
)
PRIMARY KEY advertiser_id
SOURCE(CLICKHOUSE(TABLE discounts))
LIFETIME(MIN 600 MAX 900)
LAYOUT(RANGE_HASHED(RANGE_LOOKUP_STRATEGY 'min'))
RANGE(MIN discount_start_date MAX discount_end_date);

select dictGet('discounts_dict', 'amount', 1, toDate('2015-01-14')) res;
┌─res─┐
│ 0.1 │ -- the only one range is matching: 2015-01-01 - Null
└─────┘

select dictGet('discounts_dict', 'amount', 1, toDate('2015-01-16')) res;
┌─res─┐
│ 0.1 │ -- two ranges are matching, range_min 2015-01-01 (0.1) is less than 2015-01-15 (0.2)
└─────┘

select dictGet('discounts_dict', 'amount', 2, toDate('2015-01-06')) res;
┌─res─┐
│ 0.3 │ -- two ranges are matching, range_min 2015-01-01 (0.3) is less than 2015-01-04 (0.4)
└─────┘

select dictGet('discounts_dict', 'amount', 3, toDate('2015-01-01')) res;
┌─res─┐
│ 0.6 │ -- two ranges are matching, range_min are equal, 2015-01-10 (0.6) is less than 2015-01-15 (0.5)
└─────┘

complex_key_range_hashed

The dictionary is stored in memory in the form of a hash table with an ordered array of ranges and their corresponding values (see range_hashed). This type of storage is for use with composite keys.

Configuration example:

CREATE DICTIONARY range_dictionary
(
  CountryID UInt64,
  CountryKey String,
  StartDate Date,
  EndDate Date,
  Tax Float64 DEFAULT 0.2
)
PRIMARY KEY CountryID, CountryKey
SOURCE(CLICKHOUSE(TABLE 'date_table'))
LIFETIME(MIN 1 MAX 1000)
LAYOUT(COMPLEX_KEY_RANGE_HASHED())
RANGE(MIN StartDate MAX EndDate);

cache

The dictionary is stored in a cache that has a fixed number of cells. These cells contain frequently used elements.

The dictionary key has the UInt64 type.

When searching for a dictionary, the cache is searched first. For each block of data, all keys that are not found in the cache or are outdated are requested from the source using SELECT attrs... FROM db.table WHERE id IN (k1, k2, ...). The received data is then written to the cache.

If keys are not found in dictionary, then update cache task is created and added into update queue. Update queue properties can be controlled with settings max_update_queue_size, update_queue_push_timeout_milliseconds, query_wait_timeout_milliseconds, max_threads_for_updates.

For cache dictionaries, the expiration lifetime of data in the cache can be set. If more time than lifetime has passed since loading the data in a cell, the cell’s value is not used and key becomes expired. The key is re-requested the next time it needs to be used. This behaviour can be configured with setting allow_read_expired_keys.

This is the least effective of all the ways to store dictionaries. The speed of the cache depends strongly on correct settings and the usage scenario. A cache type dictionary performs well only when the hit rates are high enough (recommended 99% and higher). You can view the average hit rate in the system.dictionaries table.

If setting allow_read_expired_keys is set to 1, by default 0. Then dictionary can support asynchronous updates. If a client requests keys and all of them are in cache, but some of them are expired, then dictionary will return expired keys for a client and request them asynchronously from the source.

To improve cache performance, use a subquery with LIMIT, and call the function with the dictionary externally.

All types of sources are supported.

Example of settings:

<layout>
    <cache>
        <!-- The size of the cache, in number of cells. Rounded up to a power of two. -->
        <size_in_cells>1000000000</size_in_cells>
        <!-- Allows to read expired keys. -->
        <allow_read_expired_keys>0</allow_read_expired_keys>
        <!-- Max size of update queue. -->
        <max_update_queue_size>100000</max_update_queue_size>
        <!-- Max timeout in milliseconds for push update task into queue. -->
        <update_queue_push_timeout_milliseconds>10</update_queue_push_timeout_milliseconds>
        <!-- Max wait timeout in milliseconds for update task to complete. -->
        <query_wait_timeout_milliseconds>60000</query_wait_timeout_milliseconds>
        <!-- Max threads for cache dictionary update. -->
        <max_threads_for_updates>4</max_threads_for_updates>
    </cache>
</layout>

or

LAYOUT(CACHE(SIZE_IN_CELLS 1000000000))

Set a large enough cache size. You need to experiment to select the number of cells:

1. Set some value.
2. Run queries until the cache is completely full.
3. Assess memory consumption using the system.dictionaries table.
4. Increase or decrease the number of cells until the required memory consumption is reached.

:::warning Do not use ClickHouse as a source, because it is slow to process queries with random reads. :::

complex_key_cache

This type of storage is for use with composite keys. Similar to cache.

ssd_cache

Similar to cache, but stores data on SSD and index in RAM. All cache dictionary settings related to update queue can also be applied to SSD cache dictionaries.

The dictionary key has the UInt64 type.

<layout>
    <ssd_cache>
        <!-- Size of elementary read block in bytes. Recommended to be equal to SSD's page size. -->
        <block_size>4096</block_size>
        <!-- Max cache file size in bytes. -->
        <file_size>16777216</file_size>
        <!-- Size of RAM buffer in bytes for reading elements from SSD. -->
        <read_buffer_size>131072</read_buffer_size>
        <!-- Size of RAM buffer in bytes for aggregating elements before flushing to SSD. -->
        <write_buffer_size>1048576</write_buffer_size>
        <!-- Path where cache file will be stored. -->
        <path>/var/lib/clickhouse/user_files/test_dict</path>
    </ssd_cache>
</layout>

or

LAYOUT(SSD_CACHE(BLOCK_SIZE 4096 FILE_SIZE 16777216 READ_BUFFER_SIZE 1048576
    PATH '/var/lib/clickhouse/user_files/test_dict'))

complex_key_ssd_cache

This type of storage is for use with composite keys. Similar to ssd_cache.

direct

The dictionary is not stored in memory and directly goes to the source during the processing of a request.

The dictionary key has the UInt64 type.

All types of sources, except local files, are supported.

Configuration example:

<layout>
  <direct />
</layout>

or

LAYOUT(DIRECT())

complex_key_direct

This type of storage is for use with composite keys. Similar to direct.

ip_trie

This type of storage is for mapping network prefixes (IP addresses) to metadata such as ASN.

Example

Suppose we have a table in ClickHouse that contains our IP prefixes and mappings:

CREATE TABLE my_ip_addresses (
	prefix String,
	asn UInt32,
	cca2 String
)
ENGINE = MergeTree
PRIMARY KEY prefix;

INSERT INTO my_ip_addresses VALUES
	('202.79.32.0/20', 17501, 'NP'),
    ('2620:0:870::/48', 3856, 'US'),
    ('2a02:6b8:1::/48', 13238, 'RU'),
    ('2001:db8::/32', 65536, 'ZZ')
;

Let's define an ip_trie dictionary for this table. The ip_trie layout requires a composite key:

<structure>
    <key>
        <attribute>
            <name>prefix</name>
            <type>String</type>
        </attribute>
    </key>
    <attribute>
            <name>asn</name>
            <type>UInt32</type>
            <null_value />
    </attribute>
    <attribute>
            <name>cca2</name>
            <type>String</type>
            <null_value>??</null_value>
    </attribute>
    ...
</structure>
<layout>
    <ip_trie>
        <!-- Key attribute `prefix` can be retrieved via dictGetString. -->
        <!-- This option increases memory usage. -->
        <access_to_key_from_attributes>true</access_to_key_from_attributes>
    </ip_trie>
</layout>

or

CREATE DICTIONARY my_ip_trie_dictionary (
    prefix String,
    asn UInt32,
    cca2 String DEFAULT '??'
)
PRIMARY KEY prefix
SOURCE(CLICKHOUSE(TABLE 'my_ip_addresses'))
LAYOUT(IP_TRIE)
LIFETIME(3600);

The key must have only one String type attribute that contains an allowed IP prefix. Other types are not supported yet.

For queries, you must use the same functions (dictGetT with a tuple) as for dictionaries with composite keys. The syntax is:

dictGetT('dict_name', 'attr_name', tuple(ip))

The function takes either UInt32 for IPv4, or FixedString(16) for IPv6. For example:

select dictGet('my_ip_trie_dictionary', 'asn', tuple(IPv6StringToNum('2001:db8::1')))

Other types are not supported yet. The function returns the attribute for the prefix that corresponds to this IP address. If there are overlapping prefixes, the most specific one is returned.

Data must completely fit into RAM.

Related Content

• Using dictionaries to accelerate queries