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We ran a set of 19 queries on AWS Athena and compared them to a Varada cluster. We used different use cases to illustrate the performance uplift data teams can expect over a wide range of workloads.
On average, Varada delivered x30 faster response time and as much as x70, at a minimal compute footprint. On full scan queries, Varada ran slower than Athena due to the small cluster.
Below is the detailed analysis of the queries we ran and the results.
We used a schema and query inspired by a ride sharing analysis and reporting use case. For this schema we had a single table with details from each trip that include geographic data, timeseries, and metadata such as trip fee.
For the queries we used complex predicates with various casting and field parsing to represent messy raw data. We used a variety of queries ranging from highly selective to cohort analysis and one full scan.
The queries were auto-generated by Tableau, with no rewrites.
In this use case we used a 4-node Varada cluster.
| Query | Category | Athena Response time (seconds) | Varada Response time (seconds) | Varada Performance Uplift |
| 01 | Selective | 15.09 | 0.37 | 3978% |
| 02 | Selective | 23.95 | 0.68 | 3422% |
| 03 | Selective | 48.06 | 0.68 | 6968% |
| 04 | Selective | 100.95 | 1.45 | 6862% |
| 05 | Selective | 153.40 | 3.70 | 4046% |
| 06 | Cohort | 11.51 | 3.65 | 215% |
| 07 | Cohort | 9.92 | 2.85 | 248% |
| 08 | Full scan | 25.16 | 92.10 | -73% |
We used a fluentbit compatible output format for files in S3 (CSV), and used jenkins server logs as a baseline. Logs are duplicated by 20, bringing the total size of gzipped CSVs to 200GB with 4B log rows.
For an apples-to-apples comparison we converted the log data to Parquet (compressed to about 100GB of Parquet files).
In this use case, we use a single node Varada cluster.
| Query | Description | Athena Response time (seconds) | Varada Response time (seconds) | Varada Performance Uplift |
| 09 | Count all errors (~10M matches) | 46 | 1.39 | 3209% |
| 10 | Count all dev messages (~90M) | 72 | 3.49 | 1963% |
| 11 | Count all dev messages for a specific job (largest) | 34 | 2.00 | 1600% |
| 12 | Count all errors in a specific build | 41 | 0.24 | 16083% |
TPC-DS is one of the popular benchmarks that is being used today for big-data / Data Warehouse and decision support systems (DSS). It includes a blend of queries on a wide spectrum: from interactive queries to deep analytical / full table aggregations.
While the majority of TPC-DS queries aren’t selective as the ones listed in the previous use cases, the benefits of Varada’s distributed indexing, smarter CBO and Dynamic Filtering yields highly interactive queries at lower latency for the end-users vs. Athena, including sub-second response times for interactive queries, even when tested just on a single node.
| TPC-DS Query | Category | Athena Response time (seconds) | Varada Response time (seconds) | Varada Performance Uplift |
| Q55 | Ad-hoc / Interactive | 21.1 | 1.02 | 1969% |
| Q42 | Ad-hoc / Interactive | 19.44 | 0.96 | 1925% |
| Q52 | Ad-hoc / Interactive | 19.72 | 1.07 | 1743% |
| Q96 | Ad-hoc / Interactive | 13.74 | 0.93 | 1377% |
| Q66 | Reporting / BI | 125.09 | 8.44 | 1382% |
| Q03 | Reporting / BI | 22.94 | 1.16 | 1878% |
| Q94 | Deep Analytics | 13.65 | 29.8 | -54% |
–Q01, Selective
SELECT trips_data.t_hour,
count(*)
FROM demo.trips_data trips_data
WHERE ((trips_data.lon >= -122.407608)
AND (trips_data.lon <= -122.40453960000001)
AND (trips_data.lat >= 37.752309099999998)
AND (trips_data.lat <= 37.7598585)
AND (trips_data.t_hour >= CAST (
CASE
WHEN TRY_CAST((SPLIT_PART(‘20,23’, ‘,’, 1)) AS DOUBLE) >= 0 THEN
FLOOR(TRY_CAST((SPLIT_PART(‘20,23’, ‘,’, 1)) AS DOUBLE))
ELSE CEIL(TRY_CAST((SPLIT_PART(‘20,23’, ‘,’, 1)) AS DOUBLE))
END AS BIGINT))
AND (trips_data.t_hour <= CAST (
CASE
WHEN TRY_CAST((SPLIT_PART(‘20,23’, ‘,’, 2)) AS DOUBLE) >= 0 THEN
FLOOR(TRY_CAST((SPLIT_PART(‘20,23’, ‘,’, 2)) AS DOUBLE))
ELSE CEIL(TRY_CAST((SPLIT_PART(‘20,23’, ‘,’, 2)) AS DOUBLE))
END AS BIGINT))
AND (trips_data.d_weekday >= 5)
AND (trips_data.d_weekday <= 7)
AND (trips_data.fare < 7.21)
AND (trips_data.last_point = 1))
GROUP BY 1
ORDER BY 2;
–Q02, Selective
SELECT trips_data.d_date,
trips_data.rider_age,
count(*)
FROM demo.trips_data trips_data
WHERE ((trips_data.lon >= -122.4083376)
AND (trips_data.lon <= -122.4045181)
AND (trips_data.lat >= 37.733525500000001)
AND (trips_data.lat <= 37.7416029)
AND (trips_data.t_hour >= CAST (
CASE
WHEN TRY_CAST((SPLIT_PART(‘08,12’, ‘,’, 1)) AS DOUBLE) >= 0 THEN
FLOOR(TRY_CAST((SPLIT_PART(‘08,12’, ‘,’, 1)) AS DOUBLE))
ELSE CEIL(TRY_CAST((SPLIT_PART(‘08,12’, ‘,’, 1)) AS DOUBLE))
END AS BIGINT))
AND (trips_data.t_hour <= CAST (
CASE
WHEN TRY_CAST((SPLIT_PART(‘08,12’, ‘,’, 2)) AS DOUBLE) >= 0 THEN
FLOOR(TRY_CAST((SPLIT_PART(‘08,12’, ‘,’, 2)) AS DOUBLE))
ELSE CEIL(TRY_CAST((SPLIT_PART(‘08,12’, ‘,’, 2)) AS DOUBLE))
END AS BIGINT))
AND (trips_data.rider_age <= 21)
AND (trips_data.last_point = 1))
GROUP BY 1,2
ORDER BY 1,2;
–Q03, Selective
SELECT d_date,fare
FROM demo.trips_data trips_data
WHERE ((trips_data.lon >= -122.407608) AND
(trips_data.lon <= -122.40453960000001) AND
(trips_data.lat >= 37.752309099999998) AND
(trips_data.lat <= 37.7598585) AND
(trips_data.rider_id = 2735610) AND
(trips_data.d_weekday >= 0) AND (trips_data.d_weekday <= 4) AND
(trips_data.fare >= 7.21) AND
(trips_data.last_point = 1));
–Q04, Selective Join (Dynamic Filtering)
SELECT
count(distinct a.tripid) as cnt_trips,
min(a.fare) as min_fare, max(a.fare) as max_fare
FROM demo.trips_data AS a
INNER JOIN
(SELECT *
FROM demo.trips_data
WHERE tripid=200607076) AS b
ON a.rider_id=b.rider_id;
–Q05, Selective Join (Dynamic Filtering)
SELECT a.d_date,
a.driver_first,
a.driver_last
FROM demo.trips_data trips_data
INNER JOIN
(SELECT *
FROM demo.trips_data trips_data
WHERE rider_first= ‘Parker’
AND rider_last=’DAVIS’) a
ON trips_data.rider_id = a.rider_id
WHERE a.rider_age=59
GROUP BY 1,2,3;
–Q06, Cohort
SELECT trips_per_week.cnt AS cnt,
trips_per_week.trips_per_week AS trips_per_week
FROM
(SELECT t /(date_diff(‘day’, cast(‘2020-05-01’ AS date), cast(‘2020-07-30’ AS date))/ 7.0) AS trips_per_week, count(rider_id) AS cnt
FROM
(SELECT rider_id,
count(distinct tripid) AS t
FROM demo.trips_data as trips_data
WHERE d_date <= cast(‘2018-01-01’ AS date)
AND d_date >= cast(‘2018-03-30’ AS date)
AND rider_age = 19
AND d_weekday = 2
AND leg_duration > 4
GROUP BY rider_id)
GROUP BY t
ORDER BY t limit 50 ) trips_per_week
GROUP BY 1, 2
limit 1;
–Q07, Cohort
SELECT trips_data.rider_age AS rider_age,
trips_data.rider_gender AS rider_gender,
SUM(1) AS cnt
FROM demo.trips_data trips_data
WHERE ((trips_data.d_date >= CAST(‘2018-01-01’ AS DATE))
AND (trips_data.d_date <= CAST(‘2018-03-31’ AS DATE))
AND rider_age = 23
AND d_weekday = 5)
GROUP BY 1, 2
limit 1;
–Q08, Full Scan
SELECT year(d_date),
month(d_date),
rider_age,
count(distinct tripid)
FROM demo.trips_data as trips_data
GROUP BY 1,2,3
ORDER BY 1,2;
–Q09, Count all errors (~10M matches)
SELECT job, build, COUNT(*)
FROM jenkins_logs_parquet_x20
WHERE log LIKE ‘%error%’
GROUP BY 1,2
ORDER BY 3 DESC
LIMIT 100;
–Q10, Count all dev messages (~90M)
SELECT job, build, COUNT(*)
FROM jenkins_logs_parquet_x20
WHERE REGEXP_LIKE(log,’dev.*’)
GROUP BY 1,2
ORDER BY 3 DESC
LIMIT 100;
–Q11, Count all dev messages for a specific job (largest)
SELECT job, build, COUNT(*)
FROM jenkins_logs_parquet_x20
WHERE regexp_like(log,’dev.*’) AND job=’new/branches/develop’
GROUP BY 1,2
ORDER BY 3 DESC
LIMIT 100;
–Q12, Count all errors in a specific build
SELECT job, build, COUNT(*)
FROM jenkins_logs_parquet_x20
WHERE log LIKE ‘%error%’ AND build =’74’
GROUP BY 1,2
ORDER BY 3 DESC LIMIT 100;
–Q55 (TPC-DS, Ad-hoc / Interactive)
SELECT i_brand_id brand_id ,
i_brand brand ,
sum(ss_ext_sales_price) ext_price
FROM date_dim , store_sales , item
WHERE (d_date_sk = ss_sold_date_sk)
AND (ss_item_sk = i_item_sk) AND(i_manager_id = 28)
AND (d_moy = 11)
AND (d_year = 1999)
GROUP BY i_brand, i_brand_id
ORDER BY ext_price DESC, i_brand_id ASC
LIMIT 100
–Q42 (TPC-DS, Ad-hoc / Interactive)
SELECT dt.d_year ,
item.i_category_id ,
item.i_category ,
sum(ss_ext_sales_price)
FROM date_dim dt , store_sales , item
WHERE (dt.d_date_sk = store_sales.ss_sold_date_sk)
AND (store_sales.ss_item_sk = item.i_item_sk)
AND (item.i_manager_id = 1) AND(dt.d_moy = 11)
AND (dt.d_year = 2000)
GROUP BY dt.d_year, item.i_category_id, item.i_category
ORDER BY sum(ss_ext_sales_price) DESC, dt.d_year ASC, item.i_category_id ASC, item.i_category ASC
LIMIT 100
–Q52 (TPC-DS, Ad-hoc / Interactive)
SELECT dt.d_year ,
item.i_brand_id brand_id ,
item.i_brand brand ,
sum(ss_ext_sales_price) ext_price
FROM date_dim dt , store_sales , item
WHERE (dt.d_date_sk = store_sales.ss_sold_date_sk)
AND (store_sales.ss_item_sk = item.i_item_sk)
AND (item.i_manager_id = 1)
AND (dt.d_moy = 11)
AND (dt.d_year = 2000)
GROUP BY dt.d_year, item.i_brand, item.i_brand_id
ORDER BY dt.d_year ASC, ext_price DESC, brand_id ASC
LIMIT 100
–Q96 (TPC-DS, Ad-hoc / Interactive)
SELECT count(*)
FROM store_sales , household_demographics , time_dim , store
WHERE (ss_sold_time_sk = time_dim.t_time_sk)
AND (ss_hdemo_sk = household_demographics.hd_demo_sk)
AND (ss_store_sk = s_store_sk)
AND (time_dim.t_hour = 20) AND(time_dim.t_minute >= 30)
AND (household_demographics.hd_dep_count = 7)
AND (store.s_store_name = ‘ese’)
ORDER BY count(*) ASC
LIMIT 100
–Q66 (TPCDS, Reporting / BI)
SELECT w_warehouse_name ,
w_warehouse_sq_ft ,
w_city ,
w_county ,
w_state ,
w_country ,
ship_carriers ,
year ,
sum(jan_sales) jan_sales ,
sum(feb_sales) feb_sales ,
sum(mar_sales) mar_sales ,
sum(apr_sales) apr_sales ,
sum(may_sales) may_sales ,
sum(jun_sales) jun_sales ,
sum(jul_sales) jul_sales ,
sum(aug_sales) aug_sales ,
sum(sep_sales) sep_sales ,
sum(oct_sales) oct_sales ,
sum(nov_sales) nov_sales ,
sum(dec_sales) dec_sales ,
sum((jan_sales / w_warehouse_sq_ft)) jan_sales_per_sq_foot ,
sum((feb_sales / w_warehouse_sq_ft)) feb_sales_per_sq_foot ,
sum((mar_sales / w_warehouse_sq_ft)) mar_sales_per_sq_foot ,
sum((apr_sales / w_warehouse_sq_ft)) apr_sales_per_sq_foot ,
sum((may_sales / w_warehouse_sq_ft)) may_sales_per_sq_foot ,
sum((jun_sales / w_warehouse_sq_ft)) jun_sales_per_sq_foot ,
sum((jul_sales / w_warehouse_sq_ft)) jul_sales_per_sq_foot ,
sum((aug_sales / w_warehouse_sq_ft)) aug_sales_per_sq_foot ,
sum((sep_sales / w_warehouse_sq_ft)) sep_sales_per_sq_foot ,
sum((oct_sales / w_warehouse_sq_ft)) oct_sales_per_sq_foot ,
sum((nov_sales / w_warehouse_sq_ft)) nov_sales_per_sq_foot ,
sum((dec_sales / w_warehouse_sq_ft)) dec_sales_per_sq_foot ,
sum(jan_net) jan_net ,
sum(feb_net) feb_net ,
sum(mar_net) mar_net ,
sum(apr_net) apr_net ,
sum(may_net) may_net ,
sum(jun_net) jun_net ,
sum(jul_net) jul_net ,
sum(aug_net) aug_net ,
sum(sep_net) sep_net ,
sum(oct_net) oct_net ,
sum(nov_net) nov_net ,
sum(dec_net) dec_net
FROM
(SELECT
w_warehouse_name ,
w_warehouse_sq_ft ,
w_city ,
w_county ,
w_state ,
w_country ,
concat(concat(‘DHL’, ‘,’), ‘BARIAN’) ship_carriers , d_year YEAR ,
sum ((CASE WHEN(d_moy = 1) THEN (ws_ext_sales_price * ws_quantity) ELSE 0 END)) jan_sales ,
sum ((CASE WHEN(d_moy = 2) THEN (ws_ext_sales_price * ws_quantity) ELSE 0 END)) feb_sales ,
sum ((CASE WHEN(d_moy = 3) THEN (ws_ext_sales_price * ws_quantity) ELSE 0 END)) mar_sales ,
sum ((CASE WHEN(d_moy = 4) THEN (ws_ext_sales_price * ws_quantity) ELSE 0 END)) apr_sales ,
sum ((CASE WHEN(d_moy = 5) THEN (ws_ext_sales_price * ws_quantity) ELSE 0 END)) may_sales ,
sum ((CASE WHEN(d_moy = 6) THEN (ws_ext_sales_price * ws_quantity) ELSE 0 END)) jun_sales ,
sum ((CASE WHEN(d_moy = 7) THEN (ws_ext_sales_price * ws_quantity) ELSE 0 END)) jul_sales ,
sum ((CASE WHEN(d_moy = 8) THEN (ws_ext_sales_price * ws_quantity) ELSE 0 END)) aug_sales ,
sum ((CASE WHEN(d_moy = 9) THEN (ws_ext_sales_price * ws_quantity) ELSE 0 END)) sep_sales ,
sum ((CASE WHEN(d_moy = 10) THEN (ws_ext_sales_price * ws_quantity) ELSE 0 END)) oct_sales ,
sum ((CASE WHEN(d_moy = 11) THEN (ws_ext_sales_price * ws_quantity) ELSE 0 END)) nov_sales ,
sum ((CASE WHEN(d_moy = 12) THEN (ws_ext_sales_price * ws_quantity) ELSE 0 END)) dec_sales ,
sum ((CASE WHEN(d_moy = 1) THEN (ws_net_paid * ws_quantity) ELSE 0 END)) jan_net ,
sum ((CASE WHEN(d_moy = 2) THEN (ws_net_paid * ws_quantity) ELSE 0 END)) feb_net ,
sum ((CASE WHEN(d_moy = 3) THEN (ws_net_paid * ws_quantity) ELSE 0 END)) mar_net ,
sum ((CASE WHEN(d_moy = 4) THEN (ws_net_paid * ws_quantity) ELSE 0 END)) apr_net ,
sum ((CASE WHEN(d_moy = 5) THEN (ws_net_paid * ws_quantity) ELSE 0 END)) may_net ,
sum ((CASE WHEN(d_moy = 6) THEN (ws_net_paid * ws_quantity) ELSE 0 END)) jun_net ,
sum ((CASE WHEN(d_moy = 7) THEN (ws_net_paid * ws_quantity) ELSE 0 END)) jul_net ,
sum ((CASE WHEN(d_moy = 8) THEN (ws_net_paid * ws_quantity) ELSE 0 END)) aug_net ,
sum ((CASE WHEN(d_moy = 9) THEN (ws_net_paid * ws_quantity) ELSE 0 END)) sep_net ,
sum ((CASE WHEN(d_moy = 10) THEN (ws_net_paid * ws_quantity) ELSE 0 END)) oct_net ,
sum ((CASE WHEN(d_moy = 11) THEN (ws_net_paid * ws_quantity) ELSE 0 END)) nov_net ,
sum ((CASE WHEN(d_moy = 12) THEN (ws_net_paid * ws_quantity) ELSE 0 END)) dec_net
FROM web_sales , warehouse , date_dim , time_dim , ship_mode
WHERE (ws_warehouse_sk = w_warehouse_sk)
AND (ws_sold_date_sk = d_date_sk)
AND (ws_sold_time_sk = t_time_sk)
AND (ws_ship_mode_sk = sm_ship_mode_sk)
AND (d_year = 2001)
AND (t_time BETWEEN 30838 AND(30838 + 28800))
AND (sm_carrier IN(‘DHL’ , ‘BARIAN’))
GROUP BY w_warehouse_name, w_warehouse_sq_ft, w_city, w_county, w_state, w_country, d_year
UNION ALL
SELECT
w_warehouse_name ,
w_warehouse_sq_ft ,
w_city ,
w_county ,
w_state ,
w_country ,
concat(concat(‘DHL’, ‘,’), ‘BARIAN’) ship_carriers , d_year YEAR ,
sum((CASE WHEN(d_moy = 1) THEN (cs_sales_price * cs_quantity) ELSE 0 END)) jan_sales ,
sum((CASE WHEN(d_moy = 2) THEN (cs_sales_price * cs_quantity) ELSE 0 END)) feb_sales ,
sum((CASE WHEN(d_moy = 3) THEN (cs_sales_price * cs_quantity) ELSE 0 END)) mar_sales ,
sum((CASE WHEN(d_moy = 4) THEN (cs_sales_price * cs_quantity) ELSE 0 END)) apr_sales ,
sum((CASE WHEN(d_moy = 5) THEN (cs_sales_price * cs_quantity) ELSE 0 END)) may_sales ,
sum((CASE WHEN(d_moy = 6) THEN (cs_sales_price * cs_quantity) ELSE 0 END)) jun_sales ,
sum((CASE WHEN(d_moy = 7) THEN (cs_sales_price * cs_quantity) ELSE 0 END)) jul_sales ,
sum((CASE WHEN(d_moy = 8) THEN (cs_sales_price * cs_quantity) ELSE 0 END)) aug_sales ,
sum((CASE WHEN(d_moy = 9) THEN (cs_sales_price * cs_quantity) ELSE 0 END)) sep_sales ,
sum((CASE WHEN(d_moy = 10) THEN (cs_sales_price * cs_quantity) ELSE 0 END)) oct_sales ,
sum((CASE WHEN(d_moy = 11) THEN (cs_sales_price * cs_quantity) ELSE 0 END)) nov_sales ,
sum((CASE WHEN(d_moy = 12) THEN (cs_sales_price * cs_quantity) ELSE 0 END)) dec_sales ,
sum((CASE WHEN(d_moy = 1) THEN (cs_net_paid_inc_tax * cs_quantity) ELSE 0 END)) jan_net ,
sum((CASE WHEN(d_moy = 2) THEN (cs_net_paid_inc_tax * cs_quantity) ELSE 0 END)) feb_net ,
sum((CASE WHEN(d_moy = 3) THEN (cs_net_paid_inc_tax * cs_quantity) ELSE 0 END)) mar_net ,
sum((CASE WHEN(d_moy = 4) THEN (cs_net_paid_inc_tax * cs_quantity) ELSE 0 END)) apr_net ,
sum((CASE WHEN(d_moy = 5) THEN (cs_net_paid_inc_tax * cs_quantity) ELSE 0 END)) may_net ,
sum((CASE WHEN(d_moy = 6) THEN (cs_net_paid_inc_tax * cs_quantity) ELSE 0 END)) jun_net ,
sum((CASE WHEN(d_moy = 7) THEN (cs_net_paid_inc_tax * cs_quantity) ELSE 0 END)) jul_net ,
sum((CASE WHEN(d_moy = 8) THEN (cs_net_paid_inc_tax * cs_quantity) ELSE 0 END)) aug_net ,
sum((CASE WHEN(d_moy = 9) THEN (cs_net_paid_inc_tax * cs_quantity) ELSE 0 END)) sep_net ,
sum((CASE WHEN(d_moy = 10) THEN (cs_net_paid_inc_tax * cs_quantity) ELSE 0 END)) oct_net ,
sum((CASE WHEN(d_moy = 11) THEN (cs_net_paid_inc_tax * cs_quantity) ELSE 0 END)) nov_net ,
sum((CASE WHEN(d_moy = 12) THEN (cs_net_paid_inc_tax * cs_quantity) ELSE 0 END)) dec_net
FROM catalog_sales , warehouse , date_dim , time_dim , ship_mode
WHERE (cs_warehouse_sk = w_warehouse_sk)
AND (cs_sold_date_sk = d_date_sk)
AND (cs_sold_time_sk = t_time_sk)
AND (cs_ship_mode_sk = sm_ship_mode_sk)
AND (d_year = 2001)
AND (t_time BETWEEN 30838 AND (30838 + 28800))
AND (sm_carrier IN (‘DHL’ , ‘BARIAN’))
GROUP BY w_warehouse_name, w_warehouse_sq_ft, w_city, w_county, w_state, w_country, d_year ) x
GROUP BY w_warehouse_name, w_warehouse_sq_ft, w_city, w_county, w_state, w_country, ship_carriers, year
ORDER BY w_warehouse_name ASC
LIMIT 100
–Q03 (TPC-DS, Reporting / BI)
SELECT dt.d_year,
item.i_brand_id brand_id,
item.i_brand brand,
sum(ss_ext_sales_price) sum_agg
FROM date_dim dt, store_sales, item
WHERE (dt.d_date_sk = store_sales.ss_sold_date_sk)
AND (store_sales.ss_item_sk = item.i_item_sk)
AND (item.i_manufact_id = 128)
AND (dt.d_moy = 11)
GROUP BY dt.d_year, item.i_brand, item.i_brand_id
ORDER BY dt.d_year ASC, sum_agg DESC, brand_id ASC
LIMIT 100
–Q94 (TPCDS, Deep Analytics)
SELECT count(DISTINCT ws_order_number) order count ,
sum(ws_ext_ship_cost) total shipping cost ,
sum(ws_net_profit) total net profit
FROM web_sales ws1 , date_dim , customer_address , web_site WHERE(d_date
BETWEEN CAST(‘1999-2-01’ AS DATE)
AND (CAST(‘1999-2-01′ AS DATE) + INTERVAL ’60’ DAY))
AND (ws1.ws_ship_date_sk = d_date_sk)
AND (ws1.ws_ship_addr_sk = ca_address_sk)
AND (ca_state = ‘IL’)
AND (ws1.ws_web_site_sk = web_site_sk)
AND (web_company_name = ‘pri’) AND(EXISTS
(SELECT *
FROM web_sales ws2
WHERE (ws1.ws_order_number = ws2.ws_order_number)
AND (ws1.ws_warehouse_sk <> ws2.ws_warehouse_sk) ))
AND (NOT(EXISTS
(SELECT *
FROM web_returns wr1
WHERE (ws1.ws_order_number = wr1.wr_order_number) )))
ORDER BY count(DISTINCT ws_order_number) ASC
LIMIT 100
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