--- sidebar_label: OnTime Airline Flight Data description: Dataset containing the on-time performance of airline flights --- # OnTime This dataset can be obtained in two ways: - import from raw data - download of prepared partitions ## Import from Raw Data {#import-from-raw-data} Downloading data: ``` bash wget --no-check-certificate --continue https://transtats.bts.gov/PREZIP/On_Time_Reporting_Carrier_On_Time_Performance_1987_present_{1987..2021}_{1..12}.zip ``` Creating a table: ``` sql CREATE TABLE `ontime` ( `Year` UInt16, `Quarter` UInt8, `Month` UInt8, `DayofMonth` UInt8, `DayOfWeek` UInt8, `FlightDate` Date, `Reporting_Airline` String, `DOT_ID_Reporting_Airline` Int32, `IATA_CODE_Reporting_Airline` String, `Tail_Number` String, `Flight_Number_Reporting_Airline` String, `OriginAirportID` Int32, `OriginAirportSeqID` Int32, `OriginCityMarketID` Int32, `Origin` FixedString(5), `OriginCityName` String, `OriginState` FixedString(2), `OriginStateFips` String, `OriginStateName` String, `OriginWac` Int32, `DestAirportID` Int32, `DestAirportSeqID` Int32, `DestCityMarketID` Int32, `Dest` FixedString(5), `DestCityName` String, `DestState` FixedString(2), `DestStateFips` String, `DestStateName` String, `DestWac` Int32, `CRSDepTime` Int32, `DepTime` Int32, `DepDelay` Int32, `DepDelayMinutes` Int32, `DepDel15` Int32, `DepartureDelayGroups` String, `DepTimeBlk` String, `TaxiOut` Int32, `WheelsOff` Int32, `WheelsOn` Int32, `TaxiIn` Int32, `CRSArrTime` Int32, `ArrTime` Int32, `ArrDelay` Int32, `ArrDelayMinutes` Int32, `ArrDel15` Int32, `ArrivalDelayGroups` Int32, `ArrTimeBlk` String, `Cancelled` UInt8, `CancellationCode` FixedString(1), `Diverted` UInt8, `CRSElapsedTime` Int32, `ActualElapsedTime` Int32, `AirTime` Nullable(Int32), `Flights` Int32, `Distance` Int32, `DistanceGroup` UInt8, `CarrierDelay` Int32, `WeatherDelay` Int32, `NASDelay` Int32, `SecurityDelay` Int32, `LateAircraftDelay` Int32, `FirstDepTime` String, `TotalAddGTime` String, `LongestAddGTime` String, `DivAirportLandings` String, `DivReachedDest` String, `DivActualElapsedTime` String, `DivArrDelay` String, `DivDistance` String, `Div1Airport` String, `Div1AirportID` Int32, `Div1AirportSeqID` Int32, `Div1WheelsOn` String, `Div1TotalGTime` String, `Div1LongestGTime` String, `Div1WheelsOff` String, `Div1TailNum` String, `Div2Airport` String, `Div2AirportID` Int32, `Div2AirportSeqID` Int32, `Div2WheelsOn` String, `Div2TotalGTime` String, `Div2LongestGTime` String, `Div2WheelsOff` String, `Div2TailNum` String, `Div3Airport` String, `Div3AirportID` Int32, `Div3AirportSeqID` Int32, `Div3WheelsOn` String, `Div3TotalGTime` String, `Div3LongestGTime` String, `Div3WheelsOff` String, `Div3TailNum` String, `Div4Airport` String, `Div4AirportID` Int32, `Div4AirportSeqID` Int32, `Div4WheelsOn` String, `Div4TotalGTime` String, `Div4LongestGTime` String, `Div4WheelsOff` String, `Div4TailNum` String, `Div5Airport` String, `Div5AirportID` Int32, `Div5AirportSeqID` Int32, `Div5WheelsOn` String, `Div5TotalGTime` String, `Div5LongestGTime` String, `Div5WheelsOff` String, `Div5TailNum` String ) ENGINE = MergeTree PARTITION BY Year ORDER BY (IATA_CODE_Reporting_Airline, FlightDate) SETTINGS index_granularity = 8192; ``` Loading data with multiple threads: ``` bash ls -1 *.zip | xargs -I{} -P $(nproc) bash -c "echo {}; unzip -cq {} '*.csv' | sed 's/\.00//g' | clickhouse-client --input_format_with_names_use_header=0 --query='INSERT INTO ontime FORMAT CSVWithNames'" ``` (if you will have memory shortage or other issues on your server, remove the `-P $(nproc)` part) ## Download of Prepared Partitions {#download-of-prepared-partitions} ``` bash $ curl -O https://datasets.clickhouse.com/ontime/partitions/ontime.tar $ tar xvf ontime.tar -C /var/lib/clickhouse # path to ClickHouse data directory $ # check permissions of unpacked data, fix if required $ sudo service clickhouse-server restart $ clickhouse-client --query "select count(*) from datasets.ontime" ``` :::note If you will run the queries described below, you have to use the full table name, `datasets.ontime`. ::: !!! info "Info" If you are using the prepared partitions or the Online Playground replace any occurrence of `IATA_CODE_Reporting_Airline` or `IATA_CODE_Reporting_Airline AS Carrier` in the following queries with `Carrier` (see `describe ontime`). ## Queries {#queries} Q0. ``` sql SELECT avg(c1) FROM ( SELECT Year, Month, count(*) AS c1 FROM ontime GROUP BY Year, Month ); ``` Q1. The number of flights per day from the year 2000 to 2008 ``` sql SELECT DayOfWeek, count(*) AS c FROM ontime WHERE Year>=2000 AND Year<=2008 GROUP BY DayOfWeek ORDER BY c DESC; ``` Q2. The number of flights delayed by more than 10 minutes, grouped by the day of the week, for 2000-2008 ``` sql SELECT DayOfWeek, count(*) AS c FROM ontime WHERE DepDelay>10 AND Year>=2000 AND Year<=2008 GROUP BY DayOfWeek ORDER BY c DESC; ``` Q3. The number of delays by the airport for 2000-2008 ``` sql SELECT Origin, count(*) AS c FROM ontime WHERE DepDelay>10 AND Year>=2000 AND Year<=2008 GROUP BY Origin ORDER BY c DESC LIMIT 10; ``` Q4. The number of delays by carrier for 2007 ``` sql SELECT IATA_CODE_Reporting_Airline AS Carrier, count(*) FROM ontime WHERE DepDelay>10 AND Year=2007 GROUP BY Carrier ORDER BY count(*) DESC; ``` Q5. The percentage of delays by carrier for 2007 ``` sql SELECT Carrier, c, c2, c*100/c2 as c3 FROM ( SELECT IATA_CODE_Reporting_Airline AS Carrier, count(*) AS c FROM ontime WHERE DepDelay>10 AND Year=2007 GROUP BY Carrier ) q JOIN ( SELECT IATA_CODE_Reporting_Airline AS Carrier, count(*) AS c2 FROM ontime WHERE Year=2007 GROUP BY Carrier ) qq USING Carrier ORDER BY c3 DESC; ``` Better version of the same query: ``` sql SELECT IATA_CODE_Reporting_Airline AS Carrier, avg(DepDelay>10)*100 AS c3 FROM ontime WHERE Year=2007 GROUP BY Carrier ORDER BY c3 DESC ``` Q6. The previous request for a broader range of years, 2000-2008 ``` sql SELECT Carrier, c, c2, c*100/c2 as c3 FROM ( SELECT IATA_CODE_Reporting_Airline AS Carrier, count(*) AS c FROM ontime WHERE DepDelay>10 AND Year>=2000 AND Year<=2008 GROUP BY Carrier ) q JOIN ( SELECT IATA_CODE_Reporting_Airline AS Carrier, count(*) AS c2 FROM ontime WHERE Year>=2000 AND Year<=2008 GROUP BY Carrier ) qq USING Carrier ORDER BY c3 DESC; ``` Better version of the same query: ``` sql SELECT IATA_CODE_Reporting_Airline AS Carrier, avg(DepDelay>10)*100 AS c3 FROM ontime WHERE Year>=2000 AND Year<=2008 GROUP BY Carrier ORDER BY c3 DESC; ``` Q7. Percentage of flights delayed for more than 10 minutes, by year ``` sql SELECT Year, c1/c2 FROM ( select Year, count(*)*100 as c1 from ontime WHERE DepDelay>10 GROUP BY Year ) q JOIN ( select Year, count(*) as c2 from ontime GROUP BY Year ) qq USING (Year) ORDER BY Year; ``` Better version of the same query: ``` sql SELECT Year, avg(DepDelay>10)*100 FROM ontime GROUP BY Year ORDER BY Year; ``` Q8. The most popular destinations by the number of directly connected cities for various year ranges ``` sql SELECT DestCityName, uniqExact(OriginCityName) AS u FROM ontime WHERE Year >= 2000 and Year <= 2010 GROUP BY DestCityName ORDER BY u DESC LIMIT 10; ``` Q9. ``` sql SELECT Year, count(*) AS c1 FROM ontime GROUP BY Year; ``` Q10. ``` sql SELECT min(Year), max(Year), IATA_CODE_Reporting_Airline AS Carrier, count(*) AS cnt, sum(ArrDelayMinutes>30) AS flights_delayed, round(sum(ArrDelayMinutes>30)/count(*),2) AS rate FROM ontime WHERE DayOfWeek NOT IN (6,7) AND OriginState NOT IN ('AK', 'HI', 'PR', 'VI') AND DestState NOT IN ('AK', 'HI', 'PR', 'VI') AND FlightDate < '2010-01-01' GROUP by Carrier HAVING cnt>100000 and max(Year)>1990 ORDER by rate DESC LIMIT 1000; ``` Bonus: ``` sql SELECT avg(cnt) FROM ( SELECT Year,Month,count(*) AS cnt FROM ontime WHERE DepDel15=1 GROUP BY Year,Month ); SELECT avg(c1) FROM ( SELECT Year,Month,count(*) AS c1 FROM ontime GROUP BY Year,Month ); SELECT DestCityName, uniqExact(OriginCityName) AS u FROM ontime GROUP BY DestCityName ORDER BY u DESC LIMIT 10; SELECT OriginCityName, DestCityName, count() AS c FROM ontime GROUP BY OriginCityName, DestCityName ORDER BY c DESC LIMIT 10; SELECT OriginCityName, count() AS c FROM ontime GROUP BY OriginCityName ORDER BY c DESC LIMIT 10; ``` You can also play with the data in Playground, [example](https://gh-api.clickhouse.com/play?user=play#U0VMRUNUIERheU9mV2VlaywgY291bnQoKikgQVMgYwpGUk9NIG9udGltZQpXSEVSRSBZZWFyPj0yMDAwIEFORCBZZWFyPD0yMDA4CkdST1VQIEJZIERheU9mV2VlawpPUkRFUiBCWSBjIERFU0M7Cg==). This performance test was created by Vadim Tkachenko. See: - https://www.percona.com/blog/2009/10/02/analyzing-air-traffic-performance-with-infobright-and-monetdb/ - https://www.percona.com/blog/2009/10/26/air-traffic-queries-in-luciddb/ - https://www.percona.com/blog/2009/11/02/air-traffic-queries-in-infinidb-early-alpha/ - https://www.percona.com/blog/2014/04/21/using-apache-hadoop-and-impala-together-with-mysql-for-data-analysis/ - https://www.percona.com/blog/2016/01/07/apache-spark-with-air-ontime-performance-data/ - http://nickmakos.blogspot.ru/2012/08/analyzing-air-traffic-performance-with.html [Original article](https://clickhouse.com/docs/en/getting_started/example_datasets/ontime/)