ClickHouse之clickhouse-local

一直在慢慢的摸索clickhouse，以前是用rpm包安裝的，沒有發現clickhouse-local，最近在centos上面編譯成功之後發現多了clickhouse-local，那麼這個玩意是什麼鬼呢？官方的說法：

Application clickhouse-local can fast processing of local files that store tables without resorting to deployment and configuration clickhouse-server

原來就是能夠直接讀取本地的文件進行查詢，不用部署clickhouse-server。那麼下面進行測試。準備一個測試文本：

[root@db_server_yayun_01 ~]# cat a.txt 
"yy","18"
"bb","20"
[root@db_server_yayun_01 ~]# 

直接看命令，後面解釋

[root@db_server_yayun_01 ~]# clickhouse-local -N test_table  --file='a.txt' --input-format=CSV -S "user String, age Int32" -q "SELECT * from test_table FORMAT Pretty"
Read 2 rows, 30.00 B in 0.001 sec., 1913 rows/sec., 28.03 KiB/sec.
┏━━━━━━┳━━━━━┓
┃ user ┃ age ┃
┡━━━━━━╇━━━━━┩
│ yy   │  18 │
├──────┼─────┤
│ bb   │  20 │
└──────┴─────┘
[root@db_server_yayun_01 ~]# 

能夠看見查詢出來了。

-N 指定表名，若是不指定默認是table

--file 指定讀取的文件

--input-format=CSV，指定讀取文件的格式。這裏是CSV格式

-S 定義表的字段以及類型

-q 指定查詢語句。

更多的參數能夠運行clickhouse-local --help

 

上面是簡單的測試，下面來試試官方文檔提到的美國民用航空數據。

[root test_clickhouse_data]$ /root/clickhouse-local -N ontime --file='On_Time_On_Time_Performance_2017_1.csv' --input-format=CSV -S "Year String,  Quarter String,  Month String,  DayofMonth String,  DayOfWeek String,  FlightDate Date,  UniqueCarrier FixedString(7),  AirlineID String,  Carrier FixedString(2),  TailNum String,  FlightNum String,  OriginAirportID String,  OriginAirportSeqID String,  OriginCityMarketID String,  Origin FixedString(5),  OriginCityName String,  OriginState FixedString(2),  OriginStateFips String,  OriginStateName String,  OriginWac String,  DestAirportID String,  DestAirportSeqID String,  DestCityMarketID String,  Dest FixedString(5),  DestCityName String,  DestState FixedString(2),  DestStateFips String,  DestStateName String,  DestWac String,  CRSDepTime String,  DepTime String,  DepDelay String,  DepDelayMinutes String,  DepDel15 String,  DepartureDelayGroups String,  DepTimeBlk String,  TaxiOut String,  WheelsOff String,  WheelsOn String,  TaxiIn String,  CRSArrTime String,  ArrTime String,  ArrDelay String,  ArrDelayMinutes String,  ArrDel15 String,  ArrivalDelayGroups String,  ArrTimeBlk String,  Cancelled String,  CancellationCode FixedString(1),  Diverted String,  CRSElapsedTime String,  ActualElapsedTime String,  AirTime String,  Flights String,  Distance String,  DistanceGroup String,  CarrierDelay String,  WeatherDelay String,  NASDelay String,  SecurityDelay String,  LateAircraftDelay String,  FirstDepTime String,  TotalAddGTime String,  LongestAddGTime String,  DivAirportLandings String,  DivReachedDest String,  DivActualElapsedTime String,  DivArrDelay String,  DivDistance String,  Div1Airport String,  Div1AirportID String,  Div1AirportSeqID String,  Div1WheelsOn String,  Div1TotalGTime String,  Div1LongestGTime String,  Div1WheelsOff String,  Div1TailNum String,  Div2Airport String,  Div2AirportID String,  Div2AirportSeqID String,  Div2WheelsOn String,  Div2TotalGTime String,  Div2LongestGTime String,  Div2WheelsOff String,  Div2TailNum String,  Div3Airport String,  Div3AirportID String,  Div3AirportSeqID String,  Div3WheelsOn String,  Div3TotalGTime String,  Div3LongestGTime String,  Div3WheelsOff String,  Div3TailNum String,  Div4Airport String,  Div4AirportID String,  Div4AirportSeqID String,  Div4WheelsOn String,  Div4TotalGTime String,  Div4LongestGTime String,  Div4WheelsOff String,  Div4TailNum String,  Div5Airport String,  Div5AirportID String,  Div5AirportSeqID String,  Div5WheelsOn String,  Div5TotalGTime String,  Div5LongestGTime String,  Div5WheelsOff String,  Div5TailNum String" -q "SELECT DestCityName, uniqExact(OriginCityName) AS u FROM ontime GROUP BY DestCityName ORDER BY u DESC LIMIT 10 FORMAT Pretty"
Read 450017 rows, 499.62 MiB in 3.423 sec., 131473 rows/sec., 145.97 MiB/sec.
┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━┓
┃ DestCityName          ┃   u ┃
┡━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━┩
│ Atlanta, GA           │ 155 │
├───────────────────────┼─────┤
│ Chicago, IL           │ 139 │
├───────────────────────┼─────┤
│ Denver, CO            │ 118 │
├───────────────────────┼─────┤
│ Dallas/Fort Worth, TX │ 113 │
├───────────────────────┼─────┤
│ Minneapolis, MN       │ 108 │
├───────────────────────┼─────┤
│ Houston, TX           │ 105 │
├───────────────────────┼─────┤
│ Detroit, MI           │  99 │
├───────────────────────┼─────┤
│ Phoenix, AZ           │  83 │
├───────────────────────┼─────┤
│ Salt Lake City, UT    │  79 │
├───────────────────────┼─────┤
│ Newark, NJ            │  78 │
└───────────────────────┴─────┘

固然速度確定比不上導入之後查詢的速度。不過仍是很是方便。