Athena: Query exhausted resources at this scale factor. How to optimize the given query

Question

I am using the below query to execute on AWS Athena. It gives error 'Query exhausted resources at this scale factor. '. In the query stats tab, I see that the data scanned is approximately 36 GB.

 WITH session_dataset AS (
            SELECT user_id,
                max(medium) as medium,
                max(event_date) as event_date,
                session_id
            FROM view_session
            where date(event_date) <= date_add('day', - 1, current_date)
                and date(event_date) >= date_add('day', - 90, current_date)
                and category not in ('Offline Sources')
            GROUP BY user_id,
                session_id
        ),
        user_conversion AS (
            select user_id,
                session_id,
                name,
                event_date,
                has_crm,
                customer_retention_type
            from view_session
            where cohort_type = 'conversion'
                and name is not null
                and date(event_date) <= date_add('day', - 1, current_date)
                and date(event_date) >= date_add('day', - 90, current_date)
        ),

    dataset_yesterday AS (
            SELECT uc.user_id,
                uc.name,
                max(uc.has_crm) as has_crm,
                max(uc.customer_retention_type) as customer_retention_type,
                count(sd.session_id) as view_count,
                date_diff(
                    'day',
                    date(min(sd.event_date)),
                    date(max(uc.event_date))
                ) AS days_convert,
                array_agg(
                    sd.medium
                    order by sd.event_date,
                        split(sd.session_id, '_') [ 1 ] asc
                ) as medium_list
            FROM session_dataset sd,
                user_conversion uc
            where date(sd.event_date) <= date(uc.event_date)
                and date(sd.event_date) >= date_add('day', - 1, current_date)
                and uc.user_id = sd.user_id
                and split(uc.session_id, '_') [ 1 ] >= split(sd.session_id, '_') [ 1 ]
            GROUP BY uc.user_id,
                uc.session_id,
                uc.name
        ),
dataset_week AS (
            SELECT uc.user_id,
                uc.name,
                max(uc.has_crm) as has_crm,
                max(uc.customer_retention_type) as customer_retention_type,
                count(sd.session_id) as view_count,
                date_diff(
                    'day',
                    date(min(sd.event_date)),
                    date(max(uc.event_date))
                ) AS days_convert,
                array_agg(
                    sd.medium
                    order by sd.event_date,
                        split(sd.session_id, '_') [ 1 ] asc
                ) as medium_list
            FROM session_dataset sd,
                user_conversion uc
            where date(sd.event_date) <= date(uc.event_date)
                and date(sd.event_date) >= date_add('day', - 7, current_date)
                and uc.user_id = sd.user_id
                and split(uc.session_id, '_') [ 1 ] >= split(sd.session_id, '_') [ 1 ]
            GROUP BY uc.user_id,
                uc.session_id,
                uc.name
        ),
dataset_month AS (
            SELECT uc.user_id,
                uc.name,
                max(uc.has_crm) as has_crm,
                max(uc.customer_retention_type) as customer_retention_type,
                count(sd.session_id) as view_count,
                date_diff(
                    'day',
                    date(min(sd.event_date)),
                    date(max(uc.event_date))
                ) AS days_convert,
                array_agg(
                    sd.medium
                    order by sd.event_date,
                        split(sd.session_id, '_') [ 1 ] asc
                ) as medium_list
            FROM session_dataset sd,
                user_conversion uc
            where date(sd.event_date) <= date(uc.event_date)
                and date(sd.event_date) >= date_add('day', - 30, current_date)
                and uc.user_id = sd.user_id
                and split(uc.session_id, '_') [ 1 ] >= split(sd.session_id, '_') [ 1 ]
            GROUP BY uc.user_id,
                uc.session_id,
                uc.name
        ),
 dataset_quarter AS (
        SELECT uc.user_id,
            uc.name,
            max(uc.has_crm) as has_crm,
            max(uc.customer_retention_type) as customer_retention_type,
            count(sd.session_id) as view_count,
            date_diff(
                'day',
                date(min(sd.event_date)),
                date(max(uc.event_date))
            ) AS days_convert,
            array_agg(
                sd.medium
                order by sd.event_date,
                    split(sd.session_id, '_') [ 1 ] asc
            ) as medium_list
        FROM session_dataset sd,
            user_conversion uc
        where date(sd.event_date) <= date(uc.event_date)
            and date(sd.event_date) >= date_add('day', - 90, current_date)
            and uc.user_id = sd.user_id
            and split(uc.session_id, '_') [ 1 ] >= split(sd.session_id, '_') [ 1 ]
        GROUP BY uc.user_id,
            uc.session_id,
            uc.name
       )

select 'yesterday' as window,
        name,
        sum(days_convert) as days_convert,
        count(name) as total_conversion,
        sum(view_count) as total_view,
        count(
            distinct IF(has_crm = '1', user_id, NULL)
        ) AS customer_count,
        count(distinct IF(has_crm != '1' or has_crm is null, user_id, NULL)) AS anonymous_customer_count,
        count(
            distinct IF(
                lower(customer_retention_type) = 'returning',
                user_id,
                NULL
            )
        ) AS returning_customer_count,
        count(
            distinct IF(
                lower(customer_retention_type) = 'new',
                user_id,
                NULL
            )
        ) AS new_customer_count,
        medium_list [ 1 ] as first_click,
        medium_list [ cardinality(medium_list) ] as last_click,
        medium_list
        from dataset_yesterday
        group by name,
            medium_list

        union all

select 'month' as window,
        name,
        sum(days_convert) as days_convert,
        count(name) as total_conversion,
        sum(view_count) as total_view,
        count(
            distinct IF(has_crm = '1', user_id, NULL)
        ) AS customer_count,
        count(distinct IF(has_crm != '1' or has_crm is null, user_id, NULL)) AS anonymous_customer_count,
        count(
            distinct IF(
                lower(customer_retention_type) = 'returning',
                user_id,
                NULL
            )
        ) AS returning_customer_count,
        count(
            distinct IF(
                lower(customer_retention_type) = 'new',
                user_id,
                NULL
            )
        ) AS new_customer_count,
        medium_list [ 1 ] as first_click,
        medium_list [ cardinality(medium_list) ] as last_click,
        medium_list
        from dataset_month
        group by name,
          medium_list

        union all

 union all

        select 'quarter' as window,
        name,
        sum(days_convert) as days_convert,
        count(name) as total_conversion,
        sum(view_count) as total_view,
        count(
            distinct IF(has_crm = '1', user_id, NULL)
        ) AS customer_count,
        count(distinct IF(has_crm != '1' or has_crm is null, user_id, NULL)) AS anonymous_customer_count,
        count(
            distinct IF(
                lower(customer_retention_type) = 'returning',
                user_id,
                NULL
            )
        ) AS returning_customer_count,
        count(
            distinct IF(
                lower(customer_retention_type) = 'new',
                user_id,
                NULL
            )
        ) AS new_customer_count,
        medium_list [ 1 ] as first_click,
        medium_list [ cardinality(medium_list) ] as last_click,
        from dataset_quarter
        group by name,
            medium_list

I found similar queries in Stack Overflow. In one post, they asked to remove the order by clause.

How can I do that in the above query?

Answer 1

I would start with PART of your time kill is probably from your WHERE clause not being sargable, meaning the date() function of the event is preventing ANY index from being used for that. And your Category NOT IN of 'Offline Sources' leaving everything else rather worthless and is killing your performance.

That said, you are pulling DATE( event_date ) indicating to me that the event_date column is actually a date/time field and looking for the event from 1-90 days old. So let me ask. would that not be the same as LESS than today at at 12am when the day cut over?

So if today is December 23 at 8:47am in the morning and you are looking for less than the current date (Dec 23), you are already truncating the time to '2022-12-23 00:00:00am'. So an event date that was Dec 22 at 11:59pm is still LESS than the current date. You dont have to do date( event_date ) to get the 2022-12-22 date, its already (time inclusive) less than Dec 23. So now you have removed one part of the non-sargable factors.

Now, do the same context fo the how far back. If you want to go back 90 days, you are going greater OR EQUAL to the date which would be '2022-09-24 00:00:00am'. So an event starting on 9/24 at 12:01am would STILL be qualified with the range in question. You have now just removed the second non-sargable and can utilize an index with simple adjustment to

where 
        event_date >= date_add('day', - 90, current_date)
    and event_date < current_date
    and category not in ('Offline Sources')

Now, on to other possible optimizing. Indexes. I would suggest the following exist

table         index
view_session  ( event_date, user_id, session_id )

For JOINS, try to avoid 30 year old SQL of comma separated from tables. Use JOIN syntax instead, it will help prevent accidental Cartesian JOINS and easier to adjust into left/right/outer joins as needed in future if ever a change needed.

In your 'yesterday' query, you have a COUNT( session_id ) but also a group by session_id which will always return 1 record per session indicating to me something is incorrect in this INTENT. If you are getting a count of sessions, you DONT want to group by them as you are building an aggregate (array_agg ) of them, so I have adjusted. Similarly with the other queries too.

I also removed the group by name since you are grouping by user_id which would indicate the name would be the same as well. So I changed to max( uc.name ) as name, since it would never change per user_id anyhow.

With your SPLIT function of session, EDIT your post and show SOME of what your data looks like from the view_Session table. It would better help what you are trying to do by 'split'ting the string and then appear to be looking for the first instance of the split string via [1] context.

split(uc.session_id, '_') [ 1 ]

The syntax of that APPEARS to not match that of the funcion call, but instead should be

split(uc.session_id, '_', 1)

indicating you want the first session id from the string regardless of how many sessions. But that would not be proper data normalization either.

Your derived queries of yesterday, week, month and quarter using the date() functionality I did not change because I dont know the comparison basis of the Session vs User Conversion Event Date. Since the derived queries dont have an index to apply against, nothing I can do about the sargability of those, but at least the pre-querying of the data should have been optimized.

Finally your queries that are doing UNION ALL, you are grouping by the NAME (not ID) and the medium list. Is this correct? What if you have 100 people with the name 'John'. Why would you be grouping by name instead of ID? Dont know, but left that alone, it's your query :)

So, from all the above points, I have revised the query to its final state below and can edit based on any issues encountered or feedback you need.

WITH session_dataset AS 
(
SELECT 
        user_id,
        session_id
        max(medium) as medium,
        max(event_date) as event_date,
    FROM 
        view_session
    where 
            event_date >= date_add('day', - 90, current_date)
        and event_date < current_date
        and category not in ('Offline Sources')
    GROUP BY 
        user_id,
        session_id
),
user_conversion AS 
(
select 
        user_id,
        session_id,
        name,
        event_date,
        has_crm,
        customer_retention_type
    from 
        view_session
    where 
            event_date >= date_add('day', - 90, current_date)
        and event_date < current_date
        and cohort_type = 'conversion'
        and name is not null
),
dataset_yesterday AS 
(
SELECT 
        uc.user_id,
        max( uc.name ) name,
        max(uc.has_crm) as has_crm,
        max(uc.customer_retention_type) as customer_retention_type,
        count(sd.session_id) as view_count,
        date_diff( 'day',
                    date(min(sd.event_date)),
                    date(max(uc.event_date))
                ) AS days_convert,
        array_agg (
            sd.medium
            order by sd.event_date,
            split(sd.session_id, '_') [ 1 ] asc
            ) as medium_list
    FROM 
        session_dataset sd
            JOIN user_conversion uc
                on sd.user_id = uc.user_id
    where 
            date(sd.event_date) <= date(uc.event_date)
        and date(sd.event_date) >= date_add('day', - 1, current_date)
        and split(uc.session_id, '_') [ 1 ] >= split(sd.session_id, '_') [ 1 ]
    GROUP BY 
        uc.user_id
),
dataset_week AS 
(
SELECT 
        uc.user_id,
        max( uc.name ) name,
        max(uc.has_crm) as has_crm,
        max(uc.customer_retention_type) as customer_retention_type,
        count(sd.session_id) as view_count,
        date_diff( 'day',
                    date(min(sd.event_date)),
                    date(max(uc.event_date))
                ) AS days_convert,
        array_agg(
                    sd.medium
                    order by sd.event_date,
                    split(sd.session_id, '_', 1 ) asc
                ) as medium_list
    FROM 
        session_dataset sd
            JOIN user_conversion uc
                on sd.user_id = uc.user_id
    where 
            date(sd.event_date) <= date(uc.event_date)
        and date(sd.event_date) >= date_add('day', - 7, current_date)
        and split(uc.session_id, '_', 1 ) >= split(sd.session_id, '_', 1 )
    GROUP BY 
        uc.user_id
),
dataset_month AS 
(
SELECT 
        uc.user_id,
        max( uc.name ) name,
        max(uc.has_crm) as has_crm,
        max(uc.customer_retention_type) as customer_retention_type,
        count(sd.session_id) as view_count,
        date_diff( 'day',
                    date(min(sd.event_date)),
                    date(max(uc.event_date))
                ) AS days_convert,
        array_agg(
                    sd.medium
                    order by sd.event_date,
                    split(sd.session_id, '_', 1 ) asc
                ) as medium_list
    FROM 
        session_dataset sd
            JOIN user_conversion uc
                on sd.user_id = uc.user_id
    where 
            date(sd.event_date) <= date(uc.event_date)
        and date(sd.event_date) >= date_add('day', - 30, current_date)
        and split(uc.session_id, '_', 1 ) >= split(sd.session_id, '_', 1 )
    GROUP BY 
        uc.user_id
),
dataset_quarter AS 
(
SELECT 
        uc.user_id,
        max( uc.name ) name,
        max(uc.has_crm) as has_crm,
        max(uc.customer_retention_type) as customer_retention_type,
        count(sd.session_id) as view_count,
        date_diff( 'day',
                    date(min(sd.event_date)),
                    date(max(uc.event_date))
                ) AS days_convert,
        array_agg(
                    sd.medium
                    order by sd.event_date,
                    split(sd.session_id, '_', 1 ) asc
                ) as medium_list
    FROM 
        session_dataset sd
            JOIN user_conversion uc
                ON sd.user_id = uc.user_id
    where 
            date(sd.event_date) <= date(uc.event_date)
        and date(sd.event_date) >= date_add('day', - 90, current_date)
        and split(uc.session_id, '_', 1 ) >= split(sd.session_id, '_', 1 )
    GROUP BY 
        uc.user_id
)

select 
        'yesterday' as window,
        name,
        sum(days_convert) as days_convert,
        count(name) as total_conversion,
        sum(view_count) as total_view,
        count( distinct IF(has_crm = '1', user_id, NULL) ) AS customer_count,
        count(distinct IF(has_crm != '1' or has_crm is null, user_id, NULL)) AS anonymous_customer_count,
        count( distinct IF( lower(customer_retention_type) = 'returning',
                            user_id, NULL )
            ) AS returning_customer_count,
        count( distinct IF( lower(customer_retention_type) = 'new',
                        user_id, NULL )
            ) AS new_customer_count,
        medium_list [ 1 ] as first_click,
        medium_list [ cardinality(medium_list) ] as last_click,
        medium_list
    from 
        dataset_yesterday
    group by 
        name,
        medium_list

union all

select 
        'month' as window,
        name,
        sum(days_convert) as days_convert,
        count(name) as total_conversion,
        sum(view_count) as total_view,
        count( distinct IF(has_crm = '1', user_id, NULL) ) AS customer_count,
        count(distinct IF(has_crm != '1' or has_crm is null, user_id, NULL)) AS anonymous_customer_count,
        count( distinct IF( lower(customer_retention_type) = 'returning',
                            user_id, NULL )
            ) AS returning_customer_count,
        count( distinct IF( lower(customer_retention_type) = 'new',
                            user_id, NULL )
            ) AS new_customer_count,
        medium_list [ 1 ] as first_click,
        medium_list [ cardinality(medium_list) ] as last_click,
        medium_list
    from 
        dataset_month
    group by 
        name,
        medium_list

union all

select 
        'quarter' as window,
        name,
        sum(days_convert) as days_convert,
        count(name) as total_conversion,
        sum(view_count) as total_view,
        count( distinct IF(has_crm = '1', user_id, NULL) ) AS customer_count,
        count(distinct IF(has_crm != '1' or has_crm is null, user_id, NULL)) AS anonymous_customer_count,
        count( distinct IF( lower(customer_retention_type) = 'returning',
                                user_id, NULL )
            ) AS returning_customer_count,
        count( distinct IF( lower(customer_retention_type) = 'new',
                                user_id, NULL )
            ) AS new_customer_count,
        medium_list [ 1 ] as first_click,
        medium_list [ cardinality(medium_list) ] as last_click,
    from 
        dataset_quarter
    group by 
        name,
        medium_list

FEEDBACK

From your comment... I dont know how deep you are into your database, but that normalization is not anything i would suggest. Parsing and splitting a function to get parts is probably the culprit of the performance. It would probably be better to have a single session table with critical one-time values. Then a SessionEvent table that has an ID to the original session table but has the subsequent individual parts such as EventUtcTime, EventAction, WhateverELseEventSpecific.

Then, you could easily query

select
      se.EventAction,
      count(*) 
   from
      SessionEvent se
   group by
      se.EventAction

and similar for your days between, date of event cutoff, etc. Just a suggestion though and obviously not enough behind-the-scenes to offer better choices.