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ClickHouse/docs/en/getting-started/example-datasets/ontime.md

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---
slug: /en/getting-started/example-datasets/ontime
sidebar_label: OnTime Airline Flight Data
description: Dataset containing the on-time performance of airline flights
---
# OnTime
This dataset contains data from Bureau of Transportation Statistics.
## Creating a table
``` sql
CREATE TABLE `ontime`
(
`Year` UInt16,
`Quarter` UInt8,
`Month` UInt8,
`DayofMonth` UInt8,
`DayOfWeek` UInt8,
`FlightDate` Date,
`Reporting_Airline` LowCardinality(String),
`DOT_ID_Reporting_Airline` Int32,
`IATA_CODE_Reporting_Airline` LowCardinality(String),
`Tail_Number` LowCardinality(String),
`Flight_Number_Reporting_Airline` LowCardinality(String),
`OriginAirportID` Int32,
`OriginAirportSeqID` Int32,
`OriginCityMarketID` Int32,
`Origin` FixedString(5),
`OriginCityName` LowCardinality(String),
`OriginState` FixedString(2),
`OriginStateFips` FixedString(2),
`OriginStateName` LowCardinality(String),
`OriginWac` Int32,
`DestAirportID` Int32,
`DestAirportSeqID` Int32,
`DestCityMarketID` Int32,
`Dest` FixedString(5),
`DestCityName` LowCardinality(String),
`DestState` FixedString(2),
`DestStateFips` FixedString(2),
`DestStateName` LowCardinality(String),
`DestWac` Int32,
`CRSDepTime` Int32,
`DepTime` Int32,
`DepDelay` Int32,
`DepDelayMinutes` Int32,
`DepDel15` Int32,
`DepartureDelayGroups` LowCardinality(String),
`DepTimeBlk` LowCardinality(String),
`TaxiOut` Int32,
`WheelsOff` LowCardinality(String),
`WheelsOn` LowCardinality(String),
`TaxiIn` Int32,
`CRSArrTime` Int32,
`ArrTime` Int32,
`ArrDelay` Int32,
`ArrDelayMinutes` Int32,
`ArrDel15` Int32,
`ArrivalDelayGroups` LowCardinality(String),
`ArrTimeBlk` LowCardinality(String),
`Cancelled` Int8,
`CancellationCode` FixedString(1),
`Diverted` Int8,
`CRSElapsedTime` Int32,
`ActualElapsedTime` Int32,
`AirTime` Int32,
`Flights` Int32,
`Distance` Int32,
`DistanceGroup` Int8,
`CarrierDelay` Int32,
`WeatherDelay` Int32,
`NASDelay` Int32,
`SecurityDelay` Int32,
`LateAircraftDelay` Int32,
`FirstDepTime` Int16,
`TotalAddGTime` Int16,
`LongestAddGTime` Int16,
`DivAirportLandings` Int8,
`DivReachedDest` Int8,
`DivActualElapsedTime` Int16,
`DivArrDelay` Int16,
`DivDistance` Int16,
`Div1Airport` LowCardinality(String),
`Div1AirportID` Int32,
`Div1AirportSeqID` Int32,
`Div1WheelsOn` Int16,
`Div1TotalGTime` Int16,
`Div1LongestGTime` Int16,
`Div1WheelsOff` Int16,
`Div1TailNum` LowCardinality(String),
`Div2Airport` LowCardinality(String),
`Div2AirportID` Int32,
`Div2AirportSeqID` Int32,
`Div2WheelsOn` Int16,
`Div2TotalGTime` Int16,
`Div2LongestGTime` Int16,
`Div2WheelsOff` Int16,
`Div2TailNum` LowCardinality(String),
`Div3Airport` LowCardinality(String),
`Div3AirportID` Int32,
`Div3AirportSeqID` Int32,
`Div3WheelsOn` Int16,
`Div3TotalGTime` Int16,
`Div3LongestGTime` Int16,
`Div3WheelsOff` Int16,
`Div3TailNum` LowCardinality(String),
`Div4Airport` LowCardinality(String),
`Div4AirportID` Int32,
`Div4AirportSeqID` Int32,
`Div4WheelsOn` Int16,
`Div4TotalGTime` Int16,
`Div4LongestGTime` Int16,
`Div4WheelsOff` Int16,
`Div4TailNum` LowCardinality(String),
`Div5Airport` LowCardinality(String),
`Div5AirportID` Int32,
`Div5AirportSeqID` Int32,
`Div5WheelsOn` Int16,
`Div5TotalGTime` Int16,
`Div5LongestGTime` Int16,
`Div5WheelsOff` Int16,
`Div5TailNum` LowCardinality(String)
) ENGINE = MergeTree
ORDER BY (Year, Quarter, Month, DayofMonth, FlightDate, IATA_CODE_Reporting_Airline);
```
## 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..2022}_{1..12}.zip
```
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_csv_empty_as_default 1 --query='INSERT INTO ontime FORMAT CSVWithNames'"
```
(if you will have memory shortage or other issues on your server, remove the `-P $(nproc)` part)
## Import from a saved copy
Alternatively, you can import data from a saved copy by the following query:
```
INSERT INTO ontime SELECT * FROM s3('https://clickhouse-public-datasets.s3.amazonaws.com/ontime/csv_by_year/*.csv.gz', CSVWithNames) SETTINGS max_insert_threads = 40;
```
The snapshot was created on 2022-05-29.
## 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://play.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
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