---
toc_priority: 21
toc_title: OnTime
---

# OnTime {#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
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:

``` sql
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:

``` 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.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 "Info"
    If you will run the queries described below, you have to use the full table name, `datasets.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 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
        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:

``` sql
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

``` sql
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:

``` sql
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

``` sql
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:

``` 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), 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.tech/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.tech/docs/en/getting_started/example_datasets/ontime/) <!--hide-->