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413 lines
8.9 KiB
Markdown
413 lines
8.9 KiB
Markdown
---
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toc_priority: 21
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toc_title: OnTime
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---
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# OnTime {#ontime}
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This dataset can be obtained in two ways:
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- import from raw data
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- download of prepared partitions
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## Import from Raw Data {#import-from-raw-data}
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Downloading data:
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``` bash
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for s in `seq 1987 2018`
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do
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for m in `seq 1 12`
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do
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wget https://transtats.bts.gov/PREZIP/On_Time_Reporting_Carrier_On_Time_Performance_1987_present_${s}_${m}.zip
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done
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done
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```
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(from https://github.com/Percona-Lab/ontime-airline-performance/blob/master/download.sh )
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Creating a table:
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``` sql
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CREATE TABLE `ontime` (
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`Year` UInt16,
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`Quarter` UInt8,
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`Month` UInt8,
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`DayofMonth` UInt8,
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`DayOfWeek` UInt8,
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`FlightDate` Date,
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`UniqueCarrier` FixedString(7),
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`AirlineID` Int32,
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`Carrier` FixedString(2),
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`TailNum` String,
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`FlightNum` String,
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`OriginAirportID` Int32,
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`OriginAirportSeqID` Int32,
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`OriginCityMarketID` Int32,
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`Origin` FixedString(5),
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`OriginCityName` String,
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`OriginState` FixedString(2),
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`OriginStateFips` String,
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`OriginStateName` String,
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`OriginWac` Int32,
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`DestAirportID` Int32,
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`DestAirportSeqID` Int32,
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`DestCityMarketID` Int32,
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`Dest` FixedString(5),
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`DestCityName` String,
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`DestState` FixedString(2),
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`DestStateFips` String,
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`DestStateName` String,
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`DestWac` Int32,
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`CRSDepTime` Int32,
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`DepTime` Int32,
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`DepDelay` Int32,
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`DepDelayMinutes` Int32,
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`DepDel15` Int32,
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`DepartureDelayGroups` String,
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`DepTimeBlk` String,
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`TaxiOut` Int32,
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`WheelsOff` Int32,
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`WheelsOn` Int32,
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`TaxiIn` Int32,
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`CRSArrTime` Int32,
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`ArrTime` Int32,
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`ArrDelay` Int32,
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`ArrDelayMinutes` Int32,
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`ArrDel15` Int32,
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`ArrivalDelayGroups` Int32,
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`ArrTimeBlk` String,
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`Cancelled` UInt8,
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`CancellationCode` FixedString(1),
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`Diverted` UInt8,
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`CRSElapsedTime` Int32,
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`ActualElapsedTime` Int32,
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`AirTime` Int32,
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`Flights` Int32,
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`Distance` Int32,
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`DistanceGroup` UInt8,
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`CarrierDelay` Int32,
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`WeatherDelay` Int32,
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`NASDelay` Int32,
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`SecurityDelay` Int32,
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`LateAircraftDelay` Int32,
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`FirstDepTime` String,
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`TotalAddGTime` String,
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`LongestAddGTime` String,
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`DivAirportLandings` String,
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`DivReachedDest` String,
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`DivActualElapsedTime` String,
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`DivArrDelay` String,
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`DivDistance` String,
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`Div1Airport` String,
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`Div1AirportID` Int32,
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`Div1AirportSeqID` Int32,
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`Div1WheelsOn` String,
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`Div1TotalGTime` String,
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`Div1LongestGTime` String,
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`Div1WheelsOff` String,
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`Div1TailNum` String,
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`Div2Airport` String,
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`Div2AirportID` Int32,
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`Div2AirportSeqID` Int32,
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`Div2WheelsOn` String,
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`Div2TotalGTime` String,
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`Div2LongestGTime` String,
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`Div2WheelsOff` String,
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`Div2TailNum` String,
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`Div3Airport` String,
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`Div3AirportID` Int32,
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`Div3AirportSeqID` Int32,
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`Div3WheelsOn` String,
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`Div3TotalGTime` String,
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`Div3LongestGTime` String,
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`Div3WheelsOff` String,
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`Div3TailNum` String,
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`Div4Airport` String,
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`Div4AirportID` Int32,
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`Div4AirportSeqID` Int32,
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`Div4WheelsOn` String,
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`Div4TotalGTime` String,
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`Div4LongestGTime` String,
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`Div4WheelsOff` String,
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`Div4TailNum` String,
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`Div5Airport` String,
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`Div5AirportID` Int32,
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`Div5AirportSeqID` Int32,
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`Div5WheelsOn` String,
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`Div5TotalGTime` String,
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`Div5LongestGTime` String,
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`Div5WheelsOff` String,
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`Div5TailNum` String
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) ENGINE = MergeTree
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PARTITION BY Year
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ORDER BY (Carrier, FlightDate)
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SETTINGS index_granularity = 8192;
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```
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Loading data:
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``` bash
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$ for i in *.zip; do echo $i; unzip -cq $i '*.csv' | sed 's/\.00//g' | clickhouse-client --input_format_with_names_use_header=0 --host=example-perftest01j --query="INSERT INTO ontime FORMAT CSVWithNames"; done
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```
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## Download of Prepared Partitions {#download-of-prepared-partitions}
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``` bash
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$ curl -O https://datasets.clickhouse.tech/ontime/partitions/ontime.tar
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$ tar xvf ontime.tar -C /var/lib/clickhouse # path to ClickHouse data directory
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$ # check permissions of unpacked data, fix if required
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$ sudo service clickhouse-server restart
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$ clickhouse-client --query "select count(*) from datasets.ontime"
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```
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!!! info "Info"
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If you will run the queries described below, you have to use the full table name, `datasets.ontime`.
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## Queries {#queries}
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Q0.
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``` sql
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SELECT avg(c1)
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FROM
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(
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SELECT Year, Month, count(*) AS c1
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FROM ontime
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GROUP BY Year, Month
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);
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```
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Q1. The number of flights per day from the year 2000 to 2008
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``` sql
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SELECT DayOfWeek, count(*) AS c
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FROM ontime
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WHERE Year>=2000 AND Year<=2008
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GROUP BY DayOfWeek
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ORDER BY c DESC;
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```
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Q2. The number of flights delayed by more than 10 minutes, grouped by the day of the week, for 2000-2008
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``` sql
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SELECT DayOfWeek, count(*) AS c
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FROM ontime
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WHERE DepDelay>10 AND Year>=2000 AND Year<=2008
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GROUP BY DayOfWeek
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ORDER BY c DESC;
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```
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Q3. The number of delays by the airport for 2000-2008
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``` sql
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SELECT Origin, count(*) AS c
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FROM ontime
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WHERE DepDelay>10 AND Year>=2000 AND Year<=2008
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GROUP BY Origin
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ORDER BY c DESC
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LIMIT 10;
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```
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Q4. The number of delays by carrier for 2007
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``` sql
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SELECT Carrier, count(*)
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FROM ontime
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WHERE DepDelay>10 AND Year=2007
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GROUP BY Carrier
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ORDER BY count(*) DESC;
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```
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Q5. The percentage of delays by carrier for 2007
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``` sql
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SELECT Carrier, c, c2, c*100/c2 as c3
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FROM
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(
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SELECT
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Carrier,
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count(*) AS c
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FROM ontime
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WHERE DepDelay>10
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AND Year=2007
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GROUP BY Carrier
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)
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JOIN
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(
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SELECT
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Carrier,
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count(*) AS c2
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FROM ontime
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WHERE Year=2007
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GROUP BY Carrier
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) USING Carrier
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ORDER BY c3 DESC;
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```
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Better version of the same query:
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``` sql
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SELECT Carrier, avg(DepDelay>10)*100 AS c3
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FROM ontime
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WHERE Year=2007
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GROUP BY Carrier
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ORDER BY c3 DESC
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```
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Q6. The previous request for a broader range of years, 2000-2008
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``` sql
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SELECT Carrier, c, c2, c*100/c2 as c3
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FROM
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(
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SELECT
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Carrier,
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count(*) AS c
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FROM ontime
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WHERE DepDelay>10
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AND Year>=2000 AND Year<=2008
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GROUP BY Carrier
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)
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JOIN
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(
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SELECT
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Carrier,
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count(*) AS c2
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FROM ontime
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WHERE Year>=2000 AND Year<=2008
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GROUP BY Carrier
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) USING Carrier
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ORDER BY c3 DESC;
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```
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Better version of the same query:
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``` sql
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SELECT Carrier, avg(DepDelay>10)*100 AS c3
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FROM ontime
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WHERE Year>=2000 AND Year<=2008
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GROUP BY Carrier
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ORDER BY c3 DESC;
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```
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Q7. Percentage of flights delayed for more than 10 minutes, by year
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``` sql
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SELECT Year, c1/c2
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FROM
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(
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select
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Year,
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count(*)*100 as c1
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from ontime
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WHERE DepDelay>10
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GROUP BY Year
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)
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JOIN
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(
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select
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Year,
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count(*) as c2
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from ontime
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GROUP BY Year
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) USING (Year)
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ORDER BY Year;
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```
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Better version of the same query:
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``` sql
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SELECT Year, avg(DepDelay>10)*100
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FROM ontime
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GROUP BY Year
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ORDER BY Year;
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```
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Q8. The most popular destinations by the number of directly connected cities for various year ranges
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``` sql
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SELECT DestCityName, uniqExact(OriginCityName) AS u
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FROM ontime
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WHERE Year >= 2000 and Year <= 2010
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GROUP BY DestCityName
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ORDER BY u DESC LIMIT 10;
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```
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Q9.
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``` sql
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SELECT Year, count(*) AS c1
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FROM ontime
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GROUP BY Year;
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```
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Q10.
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``` sql
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SELECT
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min(Year), max(Year), Carrier, count(*) AS cnt,
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sum(ArrDelayMinutes>30) AS flights_delayed,
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round(sum(ArrDelayMinutes>30)/count(*),2) AS rate
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FROM ontime
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WHERE
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DayOfWeek NOT IN (6,7) AND OriginState NOT IN ('AK', 'HI', 'PR', 'VI')
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AND DestState NOT IN ('AK', 'HI', 'PR', 'VI')
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AND FlightDate < '2010-01-01'
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GROUP by Carrier
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HAVING cnt>100000 and max(Year)>1990
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ORDER by rate DESC
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LIMIT 1000;
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```
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Bonus:
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``` sql
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SELECT avg(cnt)
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FROM
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(
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SELECT Year,Month,count(*) AS cnt
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FROM ontime
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WHERE DepDel15=1
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GROUP BY Year,Month
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);
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SELECT avg(c1) FROM
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(
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SELECT Year,Month,count(*) AS c1
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FROM ontime
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GROUP BY Year,Month
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);
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SELECT DestCityName, uniqExact(OriginCityName) AS u
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FROM ontime
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GROUP BY DestCityName
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ORDER BY u DESC
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LIMIT 10;
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SELECT OriginCityName, DestCityName, count() AS c
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FROM ontime
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GROUP BY OriginCityName, DestCityName
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ORDER BY c DESC
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LIMIT 10;
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SELECT OriginCityName, count() AS c
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FROM ontime
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GROUP BY OriginCityName
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ORDER BY c DESC
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LIMIT 10;
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```
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You can also play with the data in Playground, [example](https://gh-api.clickhouse.tech/play?user=play#U0VMRUNUIERheU9mV2VlaywgY291bnQoKikgQVMgYwpGUk9NIG9udGltZQpXSEVSRSBZZWFyPj0yMDAwIEFORCBZZWFyPD0yMDA4CkdST1VQIEJZIERheU9mV2VlawpPUkRFUiBCWSBjIERFU0M7Cg==).
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This performance test was created by Vadim Tkachenko. See:
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- https://www.percona.com/blog/2009/10/02/analyzing-air-traffic-performance-with-infobright-and-monetdb/
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- https://www.percona.com/blog/2009/10/26/air-traffic-queries-in-luciddb/
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- https://www.percona.com/blog/2009/11/02/air-traffic-queries-in-infinidb-early-alpha/
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- https://www.percona.com/blog/2014/04/21/using-apache-hadoop-and-impala-together-with-mysql-for-data-analysis/
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- https://www.percona.com/blog/2016/01/07/apache-spark-with-air-ontime-performance-data/
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- http://nickmakos.blogspot.ru/2012/08/analyzing-air-traffic-performance-with.html
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[Original article](https://clickhouse.tech/docs/en/getting_started/example_datasets/ontime/) <!--hide-->
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