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Star Schema Benchmark (SSB, 2009)

The Star Schema Benchmark is roughly based on the TPC-H's tables and queries but unlike TPC-H, it uses a star schema layout. The bulk of the data sits in a gigantic fact table which is surrounded by multiple small dimension tables. The queries joined the fact table with one or more dimension tables to apply filter criteria, e.g. MONTH = 'JANUARY'.

References:

First, checkout the star schema benchmark repository and compile the data generator:

git clone https://github.com/vadimtk/ssb-dbgen.git
cd ssb-dbgen
make

Then, generate the data. Parameter -s specifies the scale factor. For example, with -s 100, 600 million rows are generated.

./dbgen -s 1000 -T c
./dbgen -s 1000 -T l
./dbgen -s 1000 -T p
./dbgen -s 1000 -T s
./dbgen -s 1000 -T d

Now create tables in ClickHouse:

CREATE TABLE customer
(
C_CUSTKEY UInt32,
C_NAME String,
C_ADDRESS String,
C_CITY LowCardinality(String),
C_NATION LowCardinality(String),
C_REGION LowCardinality(String),
C_PHONE String,
C_MKTSEGMENT LowCardinality(String)
)
ENGINE = MergeTree ORDER BY (C_CUSTKEY);

CREATE TABLE lineorder
(
LO_ORDERKEY UInt32,
LO_LINENUMBER UInt8,
LO_CUSTKEY UInt32,
LO_PARTKEY UInt32,
LO_SUPPKEY UInt32,
LO_ORDERDATE Date,
LO_ORDERPRIORITY LowCardinality(String),
LO_SHIPPRIORITY UInt8,
LO_QUANTITY UInt8,
LO_EXTENDEDPRICE UInt32,
LO_ORDTOTALPRICE UInt32,
LO_DISCOUNT UInt8,
LO_REVENUE UInt32,
LO_SUPPLYCOST UInt32,
LO_TAX UInt8,
LO_COMMITDATE Date,
LO_SHIPMODE LowCardinality(String)
)
ENGINE = MergeTree PARTITION BY toYear(LO_ORDERDATE) ORDER BY (LO_ORDERDATE, LO_ORDERKEY);

CREATE TABLE part
(
P_PARTKEY UInt32,
P_NAME String,
P_MFGR LowCardinality(String),
P_CATEGORY LowCardinality(String),
P_BRAND LowCardinality(String),
P_COLOR LowCardinality(String),
P_TYPE LowCardinality(String),
P_SIZE UInt8,
P_CONTAINER LowCardinality(String)
)
ENGINE = MergeTree ORDER BY P_PARTKEY;

CREATE TABLE supplier
(
S_SUPPKEY UInt32,
S_NAME String,
S_ADDRESS String,
S_CITY LowCardinality(String),
S_NATION LowCardinality(String),
S_REGION LowCardinality(String),
S_PHONE String
)
ENGINE = MergeTree ORDER BY S_SUPPKEY;

CREATE TABLE date
(
D_DATEKEY Date,
D_DATE FixedString(18),
D_DAYOFWEEK LowCardinality(String),
D_MONTH LowCardinality(String),
D_YEAR UInt16,
D_YEARMONTHNUM UInt32,
D_YEARMONTH LowCardinality(FixedString(7)),
D_DAYNUMINWEEK UInt8,
D_DAYNUMINMONTH UInt8,
D_DAYNUMINYEAR UInt16,
D_MONTHNUMINYEAR UInt8,
D_WEEKNUMINYEAR UInt8,
D_SELLINGSEASON String,
D_LASTDAYINWEEKFL UInt8,
D_LASTDAYINMONTHFL UInt8,
D_HOLIDAYFL UInt8,
D_WEEKDAYFL UInt8
)
ENGINE = MergeTree ORDER BY D_DATEKEY;

The data can be imported as follows:

clickhouse-client --query "INSERT INTO customer FORMAT CSV" < customer.tbl
clickhouse-client --query "INSERT INTO part FORMAT CSV" < part.tbl
clickhouse-client --query "INSERT INTO supplier FORMAT CSV" < supplier.tbl
clickhouse-client --query "INSERT INTO lineorder FORMAT CSV" < lineorder.tbl
clickhouse-client --query "INSERT INTO date FORMAT CSV" < date.tbl

In many use cases of ClickHouse, multiple tables are converted into a single denormalized flat table. This step is optional, below queries are listed in their original form and in a format rewritten for the denormalized table.

SET max_memory_usage = 20000000000;

CREATE TABLE lineorder_flat
ENGINE = MergeTree ORDER BY (LO_ORDERDATE, LO_ORDERKEY)
AS SELECT
l.LO_ORDERKEY AS LO_ORDERKEY,
l.LO_LINENUMBER AS LO_LINENUMBER,
l.LO_CUSTKEY AS LO_CUSTKEY,
l.LO_PARTKEY AS LO_PARTKEY,
l.LO_SUPPKEY AS LO_SUPPKEY,
l.LO_ORDERDATE AS LO_ORDERDATE,
l.LO_ORDERPRIORITY AS LO_ORDERPRIORITY,
l.LO_SHIPPRIORITY AS LO_SHIPPRIORITY,
l.LO_QUANTITY AS LO_QUANTITY,
l.LO_EXTENDEDPRICE AS LO_EXTENDEDPRICE,
l.LO_ORDTOTALPRICE AS LO_ORDTOTALPRICE,
l.LO_DISCOUNT AS LO_DISCOUNT,
l.LO_REVENUE AS LO_REVENUE,
l.LO_SUPPLYCOST AS LO_SUPPLYCOST,
l.LO_TAX AS LO_TAX,
l.LO_COMMITDATE AS LO_COMMITDATE,
l.LO_SHIPMODE AS LO_SHIPMODE,
c.C_NAME AS C_NAME,
c.C_ADDRESS AS C_ADDRESS,
c.C_CITY AS C_CITY,
c.C_NATION AS C_NATION,
c.C_REGION AS C_REGION,
c.C_PHONE AS C_PHONE,
c.C_MKTSEGMENT AS C_MKTSEGMENT,
s.S_NAME AS S_NAME,
s.S_ADDRESS AS S_ADDRESS,
s.S_CITY AS S_CITY,
s.S_NATION AS S_NATION,
s.S_REGION AS S_REGION,
s.S_PHONE AS S_PHONE,
p.P_NAME AS P_NAME,
p.P_MFGR AS P_MFGR,
p.P_CATEGORY AS P_CATEGORY,
p.P_BRAND AS P_BRAND,
p.P_COLOR AS P_COLOR,
p.P_TYPE AS P_TYPE,
p.P_SIZE AS P_SIZE,
p.P_CONTAINER AS P_CONTAINER
FROM lineorder AS l
INNER JOIN customer AS c ON c.C_CUSTKEY = l.LO_CUSTKEY
INNER JOIN supplier AS s ON s.S_SUPPKEY = l.LO_SUPPKEY
INNER JOIN part AS p ON p.P_PARTKEY = l.LO_PARTKEY;

The queries are generated by ./qgen -s <scaling_factor>. Example queries for s = 100:

Q1.1

SELECT
sum(LO_EXTENDEDPRICE * LO_DISCOUNT) AS REVENUE
FROM
lineorder,
date
WHERE
LO_ORDERDATE = D_DATEKEY
AND D_YEAR = 1993
AND LO_DISCOUNT BETWEEN 1 AND 3
AND LO_QUANTITY < 25;

Denormalized table:

SELECT
sum(LO_EXTENDEDPRICE * LO_DISCOUNT) AS revenue
FROM
lineorder_flat
WHERE
toYear(LO_ORDERDATE) = 1993
AND LO_DISCOUNT BETWEEN 1 AND 3
AND LO_QUANTITY < 25;

Q1.2

SELECT
sum(LO_EXTENDEDPRICE * LO_DISCOUNT) AS REVENUE
FROM
lineorder,
date
WHERE
LO_ORDERDATE = D_DATEKEY
AND D_YEARMONTHNUM = 199401
AND LO_DISCOUNT BETWEEN 4 AND 6
AND LO_QUANTITY BETWEEN 26 AND 35;

Denormalized table:

SELECT
sum(LO_EXTENDEDPRICE * LO_DISCOUNT) AS revenue
FROM
lineorder_flat
WHERE
toYYYYMM(LO_ORDERDATE) = 199401
AND LO_DISCOUNT BETWEEN 4 AND 6
AND LO_QUANTITY BETWEEN 26 AND 35;

Q1.3

SELECT
sum(LO_EXTENDEDPRICE*LO_DISCOUNT) AS REVENUE
FROM
lineorder,
date
WHERE
LO_ORDERDATE = D_DATEKEY
AND D_WEEKNUMINYEAR = 6
AND D_YEAR = 1994
AND LO_DISCOUNT BETWEEN 5 AND 7
AND LO_QUANTITY BETWEEN 26 AND 35;

Denormalized table:

SELECT
sum(LO_EXTENDEDPRICE * LO_DISCOUNT) AS revenue
FROM
lineorder_flat
WHERE
toISOWeek(LO_ORDERDATE) = 6
AND toYear(LO_ORDERDATE) = 1994
AND LO_DISCOUNT BETWEEN 5 AND 7
AND LO_QUANTITY BETWEEN 26 AND 35;

Q2.1

SELECT
sum(LO_REVENUE),
D_YEAR,
P_BRAND
FROM
lineorder,
date,
part,
supplier
WHERE
LO_ORDERDATE = D_DATEKEY
AND LO_PARTKEY = P_PARTKEY
AND LO_SUPPKEY = S_SUPPKEY
AND P_CATEGORY = 'MFGR#12'
AND S_REGION = 'AMERICA'
GROUP BY
D_YEAR,
P_BRAND
ORDER BY
D_YEAR,
P_BRAND;

Denormalized table:

SELECT
sum(LO_REVENUE),
toYear(LO_ORDERDATE) AS year,
P_BRAND
FROM lineorder_flat
WHERE
P_CATEGORY = 'MFGR#12'
AND S_REGION = 'AMERICA'
GROUP BY
year,
P_BRAND
ORDER BY
year,
P_BRAND;

Q2.2

SELECT
sum(LO_REVENUE),
D_YEAR,
P_BRAND
FROM
lineorder,
date,
part,
supplier
WHERE
LO_ORDERDATE = D_DATEKEY
AND LO_PARTKEY = P_PARTKEY
AND LO_SUPPKEY = S_SUPPKEY
AND P_BRAND BETWEEN
'MFGR#2221' AND 'MFGR#2228'
AND S_REGION = 'ASIA'
GROUP BY
D_YEAR,
P_BRAND
ORDER BY
D_YEAR,
P_BRAND;

Denormalized table:

SELECT
sum(LO_REVENUE),
toYear(LO_ORDERDATE) AS year,
P_BRAND
FROM lineorder_flat
WHERE P_BRAND >= 'MFGR#2221' AND P_BRAND <= 'MFGR#2228' AND S_REGION = 'ASIA'
GROUP BY
year,
P_BRAND
ORDER BY
year,
P_BRAND;

Q2.3

SELECT
sum(LO_REVENUE),
D_YEAR,
P_BRAND
FROM
lineorder,
date,
part,
supplier
WHERE
LO_ORDERDATE = D_DATEKEY
AND LO_PARTKEY = P_PARTKEY
AND LO_SUPPKEY = S_SUPPKEY
AND P_BRAND = 'MFGR#2221'
AND S_REGION = 'EUROPE'
GROUP BY
D_YEAR,
P_BRAND
ORDER BY
D_YEAR,
P_BRAND;

Denormalized table:

SELECT
sum(LO_REVENUE),
toYear(LO_ORDERDATE) AS year,
P_BRAND
FROM lineorder_flat
WHERE P_BRAND = 'MFGR#2239' AND S_REGION = 'EUROPE'
GROUP BY
year,
P_BRAND
ORDER BY
year,
P_BRAND;

Q3.1

SELECT
C_NATION,
S_NATION,
D_YEAR,
sum(LO_REVENUE) AS REVENUE
FROM
customer,
lineorder,
supplier,
date
WHERE
LO_CUSTKEY = C_CUSTKEY
AND LO_SUPPKEY = S_SUPPKEY
AND LO_ORDERDATE = D_DATEKEY
AND C_REGION = 'ASIA' AND S_REGION = 'ASIA'
AND D_YEAR >= 1992 AND D_YEAR <= 1997
GROUP BY
C_NATION,
S_NATION,
D_YEAR
ORDER BY
D_YEAR ASC,
REVENUE DESC;

Denormalized table:

SELECT
C_NATION,
S_NATION,
toYear(LO_ORDERDATE) AS year,
sum(LO_REVENUE) AS revenue
FROM lineorder_flat
WHERE
C_REGION = 'ASIA'
AND S_REGION = 'ASIA'
AND year >= 1992
AND year <= 1997
GROUP BY
C_NATION,
S_NATION,
year
ORDER BY
year ASC,
revenue DESC;

Q3.2

SELECT
C_CITY,
S_CITY,
D_YEAR,
sum(LO_REVENUE) AS REVENUE
FROM
customer,
lineorder,
supplier,
date
WHERE
LO_CUSTKEY = C_CUSTKEY
AND LO_SUPPKEY = S_SUPPKEY
AND LO_ORDERDATE = D_DATEKEY
AND C_NATION = 'UNITED STATES'
AND S_NATION = 'UNITED STATES'
AND D_YEAR >= 1992 AND D_YEAR <= 1997
GROUP BY
C_CITY,
S_CITY,
D_YEAR
ORDER BY
D_YEAR ASC,
REVENUE DESC;

Denormalized table:

SELECT
C_CITY,
S_CITY,
toYear(LO_ORDERDATE) AS year,
sum(LO_REVENUE) AS revenue
FROM lineorder_flat
WHERE
C_NATION = 'UNITED STATES'
AND S_NATION = 'UNITED STATES'
AND year >= 1992
AND year <= 1997
GROUP BY
C_CITY,
S_CITY,
year
ORDER BY
year ASC,
revenue DESC;

Q3.3

SELECT
C_CITY,
S_CITY,
D_YEAR,
sum(LO_REVENUE) AS revenue
FROM
customer,
lineorder,
supplier,
date
WHERE
LO_CUSTKEY = C_CUSTKEY
AND LO_SUPPKEY = S_SUPPKEY
AND LO_ORDERDATE = D_DATEKEY
AND (C_CITY = 'UNITED KI1' OR C_CITY = 'UNITED KI5')
AND (S_CITY = 'UNITED KI1' OR S_CITY = 'UNITED KI5')
AND D_YEAR >= 1992
AND D_YEAR <= 1997
GROUP BY
C_CITY,
S_CITY,
D_YEAR
ORDER BY
D_YEAR ASC,
revenue DESC;

Denormalized table:

SELECT
C_CITY,
S_CITY,
toYear(LO_ORDERDATE) AS year,
sum(LO_REVENUE) AS revenue
FROM lineorder_flat
WHERE
(C_CITY = 'UNITED KI1' OR C_CITY = 'UNITED KI5')
AND (S_CITY = 'UNITED KI1' OR S_CITY = 'UNITED KI5')
AND year >= 1992
AND year <= 1997
GROUP BY
C_CITY,
S_CITY,
year
ORDER BY
year ASC,
revenue DESC;

Q3.4

SELECT
C_CITY,
S_CITY,
D_YEAR,
sum(LO_REVENUE) AS revenue
FROM
customer,
lineorder,
supplier,
date
WHERE
LO_CUSTKEY = C_CUSTKEY
AND LO_SUPPKEY = S_SUPPKEY
AND LO_ORDERDATE = D_DATEKEY
AND (C_CITY='UNITED KI1' OR C_CITY='UNITED KI5')
AND (S_CITY='UNITED KI1' OR S_CITY='UNITED KI5')
AND D_YEARMONTH = 'Dec1997'
GROUP BY
C_CITY,
S_CITY,
D_YEAR
ORDER BY
D_YEAR ASC,
revenue DESC;

Denormalized table:

SELECT
C_CITY,
S_CITY,
toYear(LO_ORDERDATE) AS year,
sum(LO_REVENUE) AS revenue
FROM lineorder_flat
WHERE
(C_CITY = 'UNITED KI1' OR C_CITY = 'UNITED KI5')
AND (S_CITY = 'UNITED KI1' OR S_CITY = 'UNITED KI5')
AND toYYYYMM(LO_ORDERDATE) = 199712
GROUP BY
C_CITY,
S_CITY,
year
ORDER BY
year ASC,
revenue DESC;

Q4.1

SELECT
D_YEAR,
C_NATION,
sum(LO_REVENUE - LO_SUPPLYCOST) AS PROFIT
FROM
date,
customer,
supplier,
part,
lineorder
WHERE
LO_CUSTKEY = C_CUSTKEY
AND LO_SUPPKEY = S_SUPPKEY
AND LO_PARTKEY = P_PARTKEY
AND LO_ORDERDATE = D_DATEKEY
AND C_REGION = 'AMERICA'
AND S_REGION = 'AMERICA'
AND (P_MFGR = 'MFGR#1' OR P_MFGR = 'MFGR#2')
GROUP BY
D_YEAR,
C_NATION
ORDER BY
D_YEAR,
C_NATION

Denormalized table:

SELECT
toYear(LO_ORDERDATE) AS year,
C_NATION,
sum(LO_REVENUE - LO_SUPPLYCOST) AS profit
FROM lineorder_flat
WHERE C_REGION = 'AMERICA' AND S_REGION = 'AMERICA' AND (P_MFGR = 'MFGR#1' OR P_MFGR = 'MFGR#2')
GROUP BY
year,
C_NATION
ORDER BY
year ASC,
C_NATION ASC;

Q4.2

SELECT
D_YEAR,
S_NATION,
P_CATEGORY,
sum(LO_REVENUE - LO_SUPPLYCOST) AS profit
FROM
date,
customer,
supplier,
part,
lineorder
WHERE
LO_CUSTKEY = C_CUSTKEY
AND LO_SUPPKEY = S_SUPPKEY
AND LO_PARTKEY = P_PARTKEY
AND LO_ORDERDATE = D_DATEKEY
AND C_REGION = 'AMERICA'
AND S_REGION = 'AMERICA'
AND (D_YEAR = 1997 OR D_YEAR = 1998)
AND (P_MFGR = 'MFGR#1' OR P_MFGR = 'MFGR#2')
GROUP BY
D_YEAR,
S_NATION,
P_CATEGORY
ORDER BY
D_YEAR,
S_NATION,
P_CATEGORY

Denormalized table:

SELECT
toYear(LO_ORDERDATE) AS year,
S_NATION,
P_CATEGORY,
sum(LO_REVENUE - LO_SUPPLYCOST) AS profit
FROM lineorder_flat
WHERE
C_REGION = 'AMERICA'
AND S_REGION = 'AMERICA'
AND (year = 1997 OR year = 1998)
AND (P_MFGR = 'MFGR#1' OR P_MFGR = 'MFGR#2')
GROUP BY
year,
S_NATION,
P_CATEGORY
ORDER BY
year ASC,
S_NATION ASC,
P_CATEGORY ASC;

Q4.3

SELECT
D_YEAR,
S_CITY,
P_BRAND,
sum(LO_REVENUE - LO_SUPPLYCOST) AS profit
FROM
date,
customer,
supplier,
part,
lineorder
WHERE
LO_CUSTKEY = C_CUSTKEY
AND LO_SUPPKEY = S_SUPPKEY
AND LO_PARTKEY = P_PARTKEY
AND LO_ORDERDATE = D_DATEKEY
AND C_REGION = 'AMERICA'
AND S_NATION = 'UNITED STATES'
AND (D_YEAR = 1997 OR D_YEAR = 1998)
AND P_CATEGORY = 'MFGR#14'
GROUP BY
D_YEAR,
S_CITY,
P_BRAND
ORDER BY
D_YEAR,
S_CITY,
P_BRAND

Denormalized table:

SELECT
toYear(LO_ORDERDATE) AS year,
S_CITY,
P_BRAND,
sum(LO_REVENUE - LO_SUPPLYCOST) AS profit
FROM
lineorder_flat
WHERE
S_NATION = 'UNITED STATES'
AND (year = 1997 OR year = 1998)
AND P_CATEGORY = 'MFGR#14'
GROUP BY
year,
S_CITY,
P_BRAND
ORDER BY
year ASC,
S_CITY ASC,
P_BRAND ASC;