DICTIONARY db.BannerDict ( bannerid UInt64, orderid UInt64 ) PRIMARY KEY bannerid SOURCE(ODBC(CONNECTION_STRING 'DSN=pgconn' DB pg TABLE banner)) LAYOUT(HASHED()) LIFETIME(MIN 1800 MAX 3600) 77 / 97 BannerTable DICTIONARY db.BannerDict ( bannerid UInt64, orderid UInt64 ) PRIMARY KEY bannerid SOURCE(ODBC(CONNECTION_STRING 'DSN=pgconn' DB pg TABLE banner)) LAYOUT(HASHED()) LIFETIME(MIN 1800 MAX 3600) SELECT dictGet( / 97 External dictionaries: Attributes and Key CREATE DICTIONARY SomeDict ( Key1 UInt64, Key2 String, V1 Int8 EXPRESSION rand() % 10, V2 Float64 DEFAULT 42 ) PRIMARY KEY Key1, Key2 V2 Key2 Key1 Key2

naturally Support For Semistructured Data 28 Example: NBA games dataset CREATE TABLE games (data String) ENGINE = MergeTree ORDER BY tuple(); SELECT JSONExtractString(data, 'teams', 1, 'name') FROM data type: Tuple( `_id.$oid` String, `date.$date` String, `teams.abbreviation` Array(String), `teams.city` Array(String), `teams.home` Array(UInt8), `teams.name` Array(String), `teams.players.ast` Array(Array(Int8)) Array(Array(Int8)), `teams.players.fg3_pct` Array(Array(String)), `teams.players.fg3a` Array(Array(Int8)), `teams.players.fg_pct` Array(Array(String)), `teams.players.fga` Array(Array(Int8)), `teams.players

naturally Support For Semistructured Data 28 Example: NBA games dataset CREATE TABLE games (data String) ENGINE = MergeTree ORDER BY tuple(); SELECT JSONExtractString(data, 'teams', 1, 'name') FROM data type: Tuple( `_id.$oid` String, `date.$date` String, `teams.abbreviation` Array(String), `teams.city` Array(String), `teams.home` Array(UInt8), `teams.name` Array(String), `teams.players.ast` Array(Array(Int8)) Array(Array(Int8)), `teams.players.fg3_pct` Array(Array(String)), `teams.players.fg3a` Array(Array(Int8)), `teams.players.fg_pct` Array(Array(String)), `teams.players.fga` Array(Array(Int8)), `teams.players

Column-Orient Model How we do CREATE TABLE demonstration.insert_view ( `Time` DateTime, `Name` String, `Age` UInt8, ..., `HeartRate` UInt8, `Humidity` Float32, ... ) ENGINE = MergeTree() PARTITION we do CREATE TABLE demonstration.insert_view ( `Time` DateTime, `Name` LowCardinality(String), `Age` UInt8, ..., `HeartRate` UInt8, `Humidity` Float32, ... ) ENGINE = MergeTree() PARTITION TABLE demonstration.test ( `time_series_interval` DateTime, `metric_name` String, `Name` String, `Age` UInt8, ..., `time_series` AggregateFunction( groupArray, Tuple(DateTime

ClickHouse2 ClickHouseN to-ch-sql 19 标签数据表定义 20 String Integer Double Date 数据模型定义 ch_label_dist_string ch_label_string_20191011 ch_label_int_20191011 ch_label_double_20191011

Going Further Begin Content Area = 16,30 27 Inline C++ in SQL SELECT udsf(' std::string udsf(std::string s) { return "hello, " + s; } ', 'world') • Compiled and linked dynamically

limit 1000 format TSVWithNames' resp = requests.get(url, data=query) string_io = io.StringIO(resp.text) table = pd.read_csv(string_io, sep="\t") 5 / 62 Table (part) 6 / 62 How to sample data You already

换为YYYYMMDD日期格式的整型，则使用按照 YYYYMMDD日期格式化后的字符形式输出作为分区 ID的取值。 l 使用其它类型 如果分区键取值既不属于整型，也不属于日期 类型，例如String、Float等。则通过128位Hash 算法取其Hash值作为分区ID的取值。 分区目录的命名规则 PartitionID_MinBlockNum_MaxBlockNum_Level • PartitionID index_granularity，默认8192 索引数据的生成规则 依照索引粒度生成索引，紧凑存储,惜字如金。 PRIMARY KEY ID 索引的查询过程 假如现在有一份测试数据，共192行记录。其中，主键ID为String类型， ID的取值从A000、A001、A002，按顺序增长，直至A192为止。 MergeTree的索引粒度index_granularity = 3。 索引的查询过程 MergeTree

limit 1000 format TSVWithNames' resp = requests.get(url, data=query) string_io = io.StringIO(resp.text) table = pd.read_csv(string_io, sep="\t") 5 / 62 Table (part) 6 / 62 How to sample data You already

байта 4. Для строк размером больше 24 байт https://www.researchgate.net/publication/339879042_SAHA_A_String_Adaptive_Hash_Table _for_Analytical_Databases Специализированные хэш-таблицы 47 47 TwoLevelHashTable