OnTime
This dataset can be obtained in two ways:
- import from raw data
- download of prepared partitions
Import from Raw Data
Downloading data:
echo https://transtats.bts.gov/PREZIP/On_Time_Reporting_Carrier_On_Time_Performance_1987_present_{1987..2021}_{1..12}.zip | xargs -P10 wget --no-check-certificate --continue
Creating a table:
CREATE TABLE `ontime` (
`Year` UInt16,
`Quarter` UInt8,
`Month` UInt8,
`DayofMonth` UInt8,
`DayOfWeek` UInt8,
`FlightDate` Date,
`UniqueCarrier` FixedString(7),
`AirlineID` Int32,
`Carrier` FixedString(2),
`TailNum` String,
`FlightNum` 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` 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 (Carrier, FlightDate)
SETTINGS index_granularity = 8192;
Loading data with multiple threads:
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
$ curl -O https://datasets.clickhouse.tech/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"
Info
If you will run the queries described below, you have to use the full table name, datasets.ontime
.
Queries
Q0.
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
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
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
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
SELECT 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
SELECT Carrier, c, c2, c*100/c2 as c3
FROM
(
SELECT
Carrier,
count(*) AS c
FROM ontime
WHERE DepDelay>10
AND Year=2007
GROUP BY Carrier
)
JOIN
(
SELECT
Carrier,
count(*) AS c2
FROM ontime
WHERE Year=2007
GROUP BY Carrier
) USING Carrier
ORDER BY c3 DESC;
Better version of the same query:
SELECT 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
SELECT Carrier, c, c2, c*100/c2 as c3
FROM
(
SELECT
Carrier,
count(*) AS c
FROM ontime
WHERE DepDelay>10
AND Year>=2000 AND Year<=2008
GROUP BY Carrier
)
JOIN
(
SELECT
Carrier,
count(*) AS c2
FROM ontime
WHERE Year>=2000 AND Year<=2008
GROUP BY Carrier
) USING Carrier
ORDER BY c3 DESC;
Better version of the same query:
SELECT 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
SELECT Year, c1/c2
FROM
(
select
Year,
count(*)*100 as c1
from ontime
WHERE DepDelay>10
GROUP BY Year
)
JOIN
(
select
Year,
count(*) as c2
from ontime
GROUP BY Year
) USING (Year)
ORDER BY Year;
Better version of the same query:
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
SELECT DestCityName, uniqExact(OriginCityName) AS u
FROM ontime
WHERE Year >= 2000 and Year <= 2010
GROUP BY DestCityName
ORDER BY u DESC LIMIT 10;
Q9.
SELECT Year, count(*) AS c1
FROM ontime
GROUP BY Year;
Q10.
SELECT
min(Year), max(Year), 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:
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.
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