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7b5d156cc5
In case you want dictionary optimized for memory, SPARSE_HASHED is not always gives you what you need. Consider the following example <UInt64, UInt16> as <Key, Value>, but this pair will also have a 6 byte padding (on amd64), so this is almost 40% of space wastage. And because of this padding, even google::sparse_hash_map, does not make picture better, in fact, sparse_hash_map is not very friendly to memory allocators (especially jemalloc). Here are some numbers for dictionary with 1e9 elements and UInt64 as key, and UInt16 as value: settings | load (sec) | read (sec) | read (million rows/s) | bytes_allocated | RSS HASHED upstream | - | - | - | - | 35GiB SPARSE_HASHED upstream | - | - | - | - | 26GiB - | - | - | - | - | - sparse_hash_map glibc hashbench | - | - | - | - | 17.5GiB sparse_hash_map packed allocator | 101.878 | 231.48 | 4.32 | - | 17.7GiB PackedHashMap | 15.514 | 42.35 | 23.61 | 20GiB | 22GiB As you can see PackedHashMap looks way more better then HASHED, and even better then SPARSE_HASHED, but slightly worse then sparse_hash_map with packed allocator (it is done with a custom patch to google sparse_hash_map). v2: rebase on top of bucket_count fix Signed-off-by: Azat Khuzhin <a.khuzhin@semrush.com>
92 lines
3.1 KiB
XML
92 lines
3.1 KiB
XML
<test>
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<substitutions>
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<substitution>
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<name>layout_suffix</name>
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<values>
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<value>HASHED</value>
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<value>SPARSE_HASHED</value>
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</values>
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</substitution>
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<substitution>
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<name>shards</name>
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<values>
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<value>1</value>
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<value>8</value>
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<value>16</value>
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</values>
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</substitution>
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</substitutions>
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<create_query>
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CREATE TABLE simple_key_dictionary_source_table
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(
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id UInt64,
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value_int UInt16
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) ENGINE = Memory
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</create_query>
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<create_query>
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CREATE TABLE complex_key_dictionary_source_table
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(
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id UInt64,
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id_key String,
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value_int UInt64
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) ENGINE = Memory
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</create_query>
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<create_query>
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CREATE DICTIONARY IF NOT EXISTS simple_key_{layout_suffix}_dictionary_s{shards}
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(
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id UInt64,
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value_int UInt64
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)
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PRIMARY KEY id
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SOURCE(CLICKHOUSE(TABLE 'simple_key_dictionary_source_table'))
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LAYOUT({layout_suffix}(SHARDS {shards}))
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LIFETIME(0)
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</create_query>
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<create_query>
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CREATE DICTIONARY IF NOT EXISTS complex_key_{layout_suffix}_dictionary_s{shards}
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(
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id UInt64,
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id_key String,
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value_int UInt64
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)
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PRIMARY KEY id, id_key
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SOURCE(CLICKHOUSE(TABLE 'complex_key_dictionary_source_table'))
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LAYOUT(COMPLEX_KEY_{layout_suffix}(SHARDS {shards}))
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LIFETIME(0)
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</create_query>
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<fill_query>INSERT INTO simple_key_dictionary_source_table SELECT number, number FROM numbers(3_000_000)</fill_query>
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<fill_query>INSERT INTO complex_key_dictionary_source_table SELECT number, toString(number), number FROM numbers(2_000_000)</fill_query>
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<fill_query>SYSTEM RELOAD DICTIONARY simple_key_{layout_suffix}_dictionary_s{shards}</fill_query>
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<fill_query>SYSTEM RELOAD DICTIONARY complex_key_{layout_suffix}_dictionary_s{shards}</fill_query>
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<query>SYSTEM RELOAD DICTIONARY simple_key_{layout_suffix}_dictionary_s{shards}</query>
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<query>SYSTEM RELOAD DICTIONARY complex_key_{layout_suffix}_dictionary_s{shards}</query>
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<query>
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WITH rand64() % 3_000_000 as key
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SELECT dictHas('default.simple_key_{layout_suffix}_dictionary_s{shards}', key)
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FROM numbers(3_000_000)
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FORMAT Null
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</query>
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<query>
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WITH (rand64() % 2_000_000, toString(rand64() % 2_000_000)) as key
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SELECT dictHas('default.complex_key_{layout_suffix}_dictionary_s{shards}', key)
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FROM numbers(2_000_000)
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FORMAT Null
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</query>
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<drop_query>DROP DICTIONARY IF EXISTS simple_key_{layout_suffix}_dictionary_s{shards}</drop_query>
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<drop_query>DROP DICTIONARY IF EXISTS complex_key_{layout_suffix}_dictionary_s{shards}</drop_query>
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<drop_query>DROP TABLE IF EXISTS simple_key_dictionary_source_table</drop_query>
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<drop_query>DROP TABLE IF EXISTS complex_key_dictionary_source_table</drop_query>
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</test>
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