Step 2: Testing System Performance of the Initial Table and Establishing a Baseline

Before and after tuning table structures, test and record the following information to compare differences in system performance:

• Load time
• Storage space occupied by tables
• Query performance

The examples in this practice are based on a dws.d2.xlarge cluster consisting of eight nodes. Because system performance is affected by many factors, clusters of the same flavor may have different results.

Record the results using the following benchmark table.

Perform the following steps to test the system performance before tuning to establish a benchmark:

1. Enter the cumulative load time for all the 11 tables in the benchmarks table in the Before column.
2. Record the storage space usage of each table.

   Determine how much disk space is used for each table using the pg_size_pretty function and record the results in base tables.

   1	SELECT T_NAME, PG_SIZE_PRETTY(PG_RELATION_SIZE(t_name)) FROM (VALUES('store_sales'),('date_dim'),('store'),('item'),('time_dim'),('promotion'),('customer_demographics'),('customer_address'),('household_demographics'),('customer'),('income_band')) AS names1(t_name);

   The following information is displayed:

   1 2 3 4 5 6 7 8 9 10 11 12 13 14

   t_name | pg_size_pretty ------------------------+---------------- store_sales | 42 GB date_dim | 11 MB store | 232 kB item | 110 MB time_dim | 11 MB promotion | 256 kB customer_demographics | 171 MB customer_address | 170 MB household_demographics | 504 kB customer | 441 MB income_band | 88 kB (11 rows)

3. Test query performance.

   Run the following queries and record the time spent on each query. The execution durations of the same query can be different, depending on the OS cache during execution. You are advised to perform several rounds of tests and select a group with average values.

   1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54

   \timing on SELECT * FROM (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 = 8 AND time_dim.t_minute >= 30 AND household_demographics.hd_dep_count = 5 AND store.s_store_name = 'ese' ORDER BY COUNT(*) ) LIMIT 100; SELECT * FROM (SELECT i_brand_id brand_id, i_brand brand, i_manufact_id, i_manufact, SUM(ss_ext_sales_price) ext_price FROM date_dim, store_sales, item,customer,customer_address,store WHERE d_date_sk = ss_sold_date_sk AND ss_item_sk = i_item_sk AND i_manager_id=8 AND d_moy=11 AND d_year=1999 AND ss_customer_sk = c_customer_sk AND c_current_addr_sk = ca_address_sk AND substr(ca_zip,1,5) <> substr(s_zip,1,5) AND ss_store_sk = s_store_sk GROUP BY i_brand ,i_brand_id ,i_manufact_id ,i_manufact ORDER BY ext_price desc ,i_brand ,i_brand_id ,i_manufact_id ,i_manufact ) LIMIT 100; SELECT * FROM (SELECT s_store_name, s_store_id, SUM(CASE WHEN (d_day_name='Sunday') THEN ss_sales_price ELSE null END) sun_sales, SUM(CASE WHEN (d_day_name='Monday') THEN ss_sales_price ELSE null END) mon_sales, SUM(CASE WHEN (d_day_name='Tuesday') THEN ss_sales_price ELSE null END) tue_sales, SUM(CASE WHEN (d_day_name='Wednesday') THEN ss_sales_price ELSE null END) wed_sales, SUM(CASE WHEN (d_day_name='Thursday') THEN ss_sales_price ELSE null END) thu_sales, SUM(CASE WHEN (d_day_name='Friday') THEN ss_sales_price ELSE null END) fri_sales, SUM(CASE WHEN (d_day_name='Saturday') THEN ss_sales_price ELSE null END) sat_sales FROM date_dim, store_sales, store WHERE d_date_sk = ss_sold_date_sk AND s_store_sk = ss_store_sk AND s_gmt_offset = -5 AND d_year = 2000 GROUP BY s_store_name, s_store_id ORDER BY s_store_name, s_store_id,sun_sales,mon_sales,tue_sales,wed_sales,thu_sales,fri_sales,sat_sales ) LIMIT 100;

After the preceding statistics are collected, the benchmark table is as follows: