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Windowing Functions In Hive

Windowing allows you to create a window on a set of data further allowing aggregation surrounding that data. Windowing in Hive is introduced from Hive 0.11. In this blog, we will be giving a demo on the windowing functions available in Hive.

Windowing in Hive includes the following functions

  • Lead

    • The number of rows to lead can optionally be specified. If the number of rows to lead is not specified, the lead is one row.

    • Returns null when the lead for the current row extends beyond the end of the window.

  • Lag

    The number of rows to lag can optionally be specified. If the number of rows to lag is not specified, the lag is one row.

    Returns null when the lag for the current row extends before the beginning of the window.

  • FIRST_VALUE

  • LAST_VALUE

The OVER clause

  • OVER with standard aggregates:
    • COUNT
    • SUM
    • MIN
    • MAX
    • AVG

OVER with a PARTITION BY statement with one or more partitioning columns.

  • OVER with PARTITION BY and ORDER BY with one or more partitioning and/or ordering columns.

Analytics functions

  • RANK
  • ROW_NUMBER
  • DENSE_RANK
  • CUME_DIST
  • PERCENT_RANK
  • NTILE

To give you a brief idea of these windowing functions in Hive, we will be using stock market data. You can download the sample stocks data from here and load into your stocks table.

Now we will create a table to load this stock market data as shown below.

create table stocks (date_ String, Ticker String, Open Double, High Double, Low Double, Close Double, Volume_for_the_day int) row format delimited fields terminated by ',';

Let us dive deeper into the window functions in Hive.

Lag

This function returns the values of the previous row. You can specify an integer offset which designates the row position else it will take the default integer offset as 1.

Here is the sample function for lag

select ticker,date_,close,lag(close,1) over(partition by ticker) as yesterday_price from acadgild.stocks

Here using lag we can display the yesterday’s closing price of the ticker. Lag is to be used with over function, inside the over function you can use partition or order by classes.

In the below screenshot, you can see the closing price of the stock for the day and the yesterday’s price.

Lead

This function returns the values from the following rows. You can specify an integer offset which designates the row position else it will take the default integer offset as 1.

Here is the sample function for lead

Now using the lead function, we will find that whether the following day’s closing price is higher or lesser than today’s and that can be done as follows.

select ticker,date_,close,case(lead(close,1) over(partition by ticker)-close)>0 when true then "higher" when false then "lesser" end as Changes from acadgild.stocks

In the below screenshot, you can see the result.

FIRST_VALUE

It returns the value of the first row from that window. With the below query, you can see the first row high price of the ticker for all the days.

select ticker,first_value(high) over(partition by ticker) as first_high from acadgild.stocks

LAST_VALUE

It is the reverse of FIRST_VALUE. It returns the value of the last row from that window. With the below query, you can see the last row high price value of the ticker for all the days.

select ticker,last_value(high) over(partition by ticker) as first_high from acadgild.stocks

Let us now see the usage of the aggregate function using Over.

Count

It returns the count of all the values for the expression written in the over clause. From the below query, we can find the number of rows present for each ticker.

select ticker,count(ticker) over(partition by ticker) as cnt from acadgild.stocks

For each partition, the count of ticker will be calculated, you can see the same in the below screen shot.

Sum

It returns the sum of all the values for the expression written in the over clause. From the below query, we can find the sum of all the closing stock prices for that particular ticker.

select ticker,sum(close) over(partition by ticker) as total from acadgild.stocks

For each ticker, the sum of all the closing prices will be calculated, you can see the same in the below screen shot.

Finding running total

Hadoop

For suppose let us take if you want to get running total of the volume_for_the_day for all the days for every ticker then you can do this with the below query.

select ticker,date_,volume_for_the_day,sum(volume_for_the_day) over(partition by ticker order by date_) as running_total from acadgild.stocks

In the above screenshot, you can see the volume_for_the_day for each day and the running total is the sum of volume_for_the_day’s that are elapsed.

Finding the percentage of each row value

Now let’s take a scenario where you need to find the percentage of the volume_for_the_day on the total volumes for that particular ticker and that can be done as follows.

select ticker,date_,volume_for_the_day,(volume_for_the_day*100/(sum(volume_for_the_day) over(partition by ticker))) from acadgild.stocks

In the above screenshot, you can see that the percentage contribution of the volumes for the day is found based on the total volume for that ticker.

Min

It returns the minimum value of the column for the rows in that over clause. From the below query, we can find the minimum closing stock price for each particular ticker.

select ticker, min(close) over(partition by ticker) as minimum from acadgild.stocks

Max

It returns the maximum value of the column for the rows in that over clause. From the below query, we can find the maximum closing stock price for each particular ticker.

select ticker, max(close) over(partition by ticker) as maximum from acadgild.stocks

AVG

It returns the average value of the column for the rows that over clause returns. From the below query, we can find the average closing stock price for each particular ticker.

select ticker, avg(close) over(partition by ticker) as maximum from acadgild.stocks

Now let us work on some Analytic functions.

Rank

The rank function will return the rank of the values as per the result set of the over clause. If two values are same then it will give the same rank to those 2 values and then for the next value, the sub-sequent rank will be skipped.

The below query will rank the closing prices of the stock for each ticker. The same you can see in the below screenshot.

select ticker,close,rank() over(partition by ticker order by close) as closing from acadgild.stocks

Row_number

Row number will return the continuous sequence of numbers for all the rows of the result set of the over clause.

From the below query, you will get the ticker, closing price and its row number for each ticker.

select ticker,close,row_number() over(partition by ticker order by close) as num from acadgild.stocks

Dense_rank

It is same as the rank() function but the difference is if any duplicate value is present then the rank will not be skipped for the subsequent rows. Each unique value will get the ranks in a sequence.

The below query will rank the closing prices of the stock for each ticker. The same you can see in the below screenshot.

select ticker,close,dense_rank() over(partition by ticker order by close) as closing from acadgild.stocks

Cume_dist

It returns the cumulative distribution of a value. It results from 0 to 1. For suppose if the total number of records are 10 then for the 1st row the cume_dist will be 1/10 and for the second 2/10 and so on till 10/10.

This cume_dist will be calculated in accordance with the result set returned by the over clause. The below query will result in the cumulative of each record for every ticker.

select ticker,cume_dist() over(partition by ticker order by close) as cummulative from acadgild.stocks

Percent_rank

It returns the percentage rank of each row within the result set of over clause. Percent_rank is calculated in accordance with the rank of the row and the calculation is as follows (rank-1)/(total_rows_in_group – 1). If the result set has only one row then the percent_rank will be 0.

The below query will calculate the percent_rank for every row in each partition and you can see the same in the below screen shot.

select ticker,close,percent_rank() over(partition by ticker order by close) as closing from acadgild.stocks

Ntile

It returns the bucket number of the particular value. For suppose if you say Ntile(5) then it will create 5 buckets based on the result set of the over clause after that it will place the first 20% of the records in the 1st bucket and so on till 5th bucket.

The below query will create 5 buckets for every ticker and the first 20% records for every ticker will be in the 1st bucket and so on.

select ticker,ntile(5) over(partition by ticker order by close ) as bucket from acadgild.stocks

In the below screenshot, you can see that 5 buckets will be created for every ticker and the least 20% closing prices will be in the first bucket and the next 20% will be in the second bucket and so on till 5th bucket for all the tickers.

This is how we can perform windowing operations in Hive.

We hope this blog helped you in understanding what are windowing functions and how to implement them in Hive. Keep visiting our site www.acadgild.com for more updates on Big Data and other technologies.

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2 Comments

  1. Sir,
    Looks like there is a little typo in creating running_total.
    Withing Partition by, the order by should not come I guess.
    If the order by is placed, then it wont generate running total as per my humble understanding.
    Please correct me if I am wrong.
    Thanks,
    Ganesh

  2. BTW, its an aawwwesome article. Nobody can explain windowing simpler than this.
    Keep up the great works.
    Thanks,
    Ganesh

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