概述
TPC-DS（Transaction Processing Performance Council - Decision Support）是一种由交易处理性能评估委员会（TPC）发布的基准测试标准，旨在评估决策支持系统（Decision Support Systems，DSS）的性能。相较于TPC-H更适合评估传统的查询和报表性能，TPC-DS包含了对数据集的分析报告、交互查询、数据挖掘等复杂应用场景，更接近真实的数据仓库业务分析场景。
本报告为您提供了云器Lakehouse与Spark SQL在TPC-DS测试集10TB规模上的测试结果，结论如下：
- 在TPC-DS 10TB规模数据集上的比较测试中，与Spark相比，云器Lakehouse展现出了显著的性能优势，其性能相当于Spark的9.51倍。
- 云器Lakehouse对Spark长耗时作业有明显性能提升。
测试环境
| 配置项 | 配置信息 |
|---|
| 服务器 | Hadoop集群：Master节点：1台阿里云ECS服务器（ecs.g8i.xlarge，4 vCPU 16 GiB）；Core节点：4台阿里云ECS服务器（ecs.g7.8xlarge，32 vCPU 128 GiB），每台服务器配置ESSD云盘300GiB*4 |
| 网络带宽 | 16Gbps |
| 软件 | Spark 3.4.2 |
| 存储服务 | 阿里云OSS对象存储 |
| 数据格式 | Parquet（Snappy压缩） |
| 配置项 | 配置信息 |
|---|
| 计算资源 | Virtual Cluster：XLarge(128vCore等效算力) |
| 软件 | 阿里云上海Region 云器Lakehouse服务 |
| 存储服务 | 托管存储，阿里云OSS对象存储 |
测试数据
| 表 | 行数 |
|---|
| call_center | 54 |
| catalog_page | 40,000 |
| catalog_returns | 1,440,033,112 |
| catalog_sales | 14,399,964,710 |
| customer | 65000000 |
| customer_address | 32,500,000 |
| customer_demographics | 1,920,800 |
| date_dim | 73,049 |
| household_demographics | 7,200 |
| income_band | 20 |
| inventory | 1311525000 |
| item | 402000 |
| promotion | 2,000 |
| reason | 70 |
| ship_mode | 20 |
| store | 1,500 |
| store_returns | 2,880,015,149 |
| store_sales | 28,799,944,153 |
| time_dim | 86,400 |
| warehouse | 25 |
| web_page | 4,002 |
| web_returns | 720,020,485 |
| web_sales | 7,199,963,324 |
| web_site | 78 |
测试过程
在测试中，我们选择了TPC-DS基准测试中的103个复杂SQL查询，对10TB的数据集进行性能测试。测试结果包括每个查询在云器Lakehouse和Spark SQL中的执行时间，以及两者的性能对比。
Spark SQL
在元数据服务中创建TPC-DS数据表，使用Parquet文件格式，分区设置与Lakehouse保持一致。
同时，从云器Lakehouse中导出TPC-DS 10TB测试数据，以数据文件形式保存至对象存储服务，以保证双方的测试数据一致。然后在Spark中使用INSERT INTO语句读取数据文件并写入Spark定义的数据表中。
- 运行TPC-DS 103个查询时，Spark添加了以下参数：
--spark 生产环境大作业必调参数之一。在处理TPCDS-10T规模的数据时，若使用默认的最大并发数200，会因规模偏小而导致大量task内存占用过高，并且极易触发shuffle spill，进而使Spark运行缓慢。经测试，将该参数值调整为2000后，观察到spill大幅减少。因此，我们决定采用2000这一参数值，以优化Spark的运行性能。
set spark.sql.shuffle.partitions = 2000;spark默认值为200
云器Lakehouse
创建集群和表
使用云器Lakehouse XLARGE Virtual Cluster在阿里云OSS上进行测试，所有表均使用默认存储格式。
create vcluster if not exists XLARGE_CLUSTER vcluster_size='XLARGE' vcluster_type='Analytics' AUTO_RESUME=TRUE AUTO_SUSPEND_IN_SECOND=300 min_replicas=1 max_replicas=1;
建表语句
drop table if exists call_center;
drop table if exists catalog_page;
drop table if exists catalog_returns;
drop table if exists catalog_sales;
drop table if exists customer;
drop table if exists customer_address;
drop table if exists customer_demographics;
drop table if exists date_dim;
drop table if exists household_demographics;
drop table if exists income_band;
drop table if exists inventory;
drop table if exists item;
drop table if exists promotion;
drop table if exists reason;
drop table if exists ship_mode;
drop table if exists store;
drop table if exists store_returns;
drop table if exists store_sales;
drop table if exists time_dim;
drop table if exists warehouse;
drop table if exists web_page;
drop table if exists web_returns;
drop table if exists web_sales;
drop table if exists web_site;
drop table if exists catalog_sales;
drop table if exists catalog_returns;
create table if not exists catalog_sales
(
cs_sold_date_sk int,
cs_sold_time_sk int,
cs_ship_date_sk int,
cs_bill_customer_sk int,
cs_bill_cdemo_sk int,
cs_bill_hdemo_sk int,
cs_bill_addr_sk int,
cs_ship_customer_sk int,
cs_ship_cdemo_sk int,
cs_ship_hdemo_sk int,
cs_ship_addr_sk int,
cs_call_center_sk int,
cs_catalog_page_sk int,
cs_ship_mode_sk int,
cs_warehouse_sk int,
cs_item_sk int,
cs_promo_sk int,
cs_order_number long,
cs_quantity int,
cs_wholesale_cost decimal(7,2),
cs_list_price decimal(7,2),
cs_sales_price decimal(7,2),
cs_ext_discount_amt decimal(7,2),
cs_ext_sales_price decimal(7,2),
cs_ext_wholesale_cost decimal(7,2),
cs_ext_list_price decimal(7,2),
cs_ext_tax decimal(7,2),
cs_coupon_amt decimal(7,2),
cs_ext_ship_cost decimal(7,2),
cs_net_paid decimal(7,2),
cs_net_paid_inc_tax decimal(7,2),
cs_net_paid_inc_ship decimal(7,2),
cs_net_paid_inc_ship_tax decimal(7,2),
cs_net_profit decimal(7,2)
) partitioned by (cs_sold_date_sk);
create table if not exists catalog_returns
(
cr_returned_date_sk int,
cr_returned_time_sk int,
cr_item_sk int,
cr_refunded_customer_sk int,
cr_refunded_cdemo_sk int,
cr_refunded_hdemo_sk int,
cr_refunded_addr_sk int,
cr_returning_customer_sk int,
cr_returning_cdemo_sk int,
cr_returning_hdemo_sk int,
cr_returning_addr_sk int,
cr_call_center_sk int,
cr_catalog_page_sk int,
cr_ship_mode_sk int,
cr_warehouse_sk int,
cr_reason_sk int,
cr_order_number long,
cr_return_quantity int,
cr_return_amount decimal(7,2),
cr_return_tax decimal(7,2),
cr_return_amt_inc_tax decimal(7,2),
cr_fee decimal(7,2),
cr_return_ship_cost decimal(7,2),
cr_refunded_cash decimal(7,2),
cr_reversed_charge decimal(7,2),
cr_store_credit decimal(7,2),
cr_net_loss decimal(7,2)
) partitioned by (cr_returned_date_sk);
create table if not exists inventory
(
inv_date_sk int,
inv_item_sk int,
inv_warehouse_sk int,
inv_quantity_on_hand int
) partitioned by (inv_date_sk);
create table if not exists store_sales
(
ss_sold_date_sk int,
ss_sold_time_sk int,
ss_item_sk int,
ss_customer_sk int,
ss_cdemo_sk int,
ss_hdemo_sk int,
ss_addr_sk int,
ss_store_sk int,
ss_promo_sk int,
ss_ticket_number long,
ss_quantity int,
ss_wholesale_cost decimal(7,2),
ss_list_price decimal(7,2),
ss_sales_price decimal(7,2),
ss_ext_discount_amt decimal(7,2),
ss_ext_sales_price decimal(7,2),
ss_ext_wholesale_cost decimal(7,2),
ss_ext_list_price decimal(7,2),
ss_ext_tax decimal(7,2),
ss_coupon_amt decimal(7,2),
ss_net_paid decimal(7,2),
ss_net_paid_inc_tax decimal(7,2),
ss_net_profit decimal(7,2)
) partitioned by (ss_sold_date_sk);
create table if not exists store_returns
(
sr_returned_date_sk int,
sr_return_time_sk int,
sr_item_sk int,
sr_customer_sk int,
sr_cdemo_sk int,
sr_hdemo_sk int,
sr_addr_sk int,
sr_store_sk int,
sr_reason_sk int,
sr_ticket_number long,
sr_return_quantity int,
sr_return_amt decimal(7,2),
sr_return_tax decimal(7,2),
sr_return_amt_inc_tax decimal(7,2),
sr_fee decimal(7,2),
sr_return_ship_cost decimal(7,2),
sr_refunded_cash decimal(7,2),
sr_reversed_charge decimal(7,2),
sr_store_credit decimal(7,2),
sr_net_loss decimal(7,2)
) partitioned by (sr_returned_date_sk);
create table if not exists web_sales
(
ws_sold_date_sk int,
ws_sold_time_sk int,
ws_ship_date_sk int,
ws_item_sk int,
ws_bill_customer_sk int,
ws_bill_cdemo_sk int,
ws_bill_hdemo_sk int,
ws_bill_addr_sk int,
ws_ship_customer_sk int,
ws_ship_cdemo_sk int,
ws_ship_hdemo_sk int,
ws_ship_addr_sk int,
ws_web_page_sk int,
ws_web_site_sk int,
ws_ship_mode_sk int,
ws_warehouse_sk int,
ws_promo_sk int,
ws_order_number long,
ws_quantity int,
ws_wholesale_cost decimal(7,2),
ws_list_price decimal(7,2),
ws_sales_price decimal(7,2),
ws_ext_discount_amt decimal(7,2),
ws_ext_sales_price decimal(7,2),
ws_ext_wholesale_cost decimal(7,2),
ws_ext_list_price decimal(7,2),
ws_ext_tax decimal(7,2),
ws_coupon_amt decimal(7,2),
ws_ext_ship_cost decimal(7,2),
ws_net_paid decimal(7,2),
ws_net_paid_inc_tax decimal(7,2),
ws_net_paid_inc_ship decimal(7,2),
ws_net_paid_inc_ship_tax decimal(7,2),
ws_net_profit decimal(7,2)
) partitioned by (ws_sold_date_sk);
create table if not exists web_returns
(
wr_returned_date_sk int,
wr_returned_time_sk int,
wr_item_sk int,
wr_refunded_customer_sk int,
wr_refunded_cdemo_sk int,
wr_refunded_hdemo_sk int,
wr_refunded_addr_sk int,
wr_returning_customer_sk int,
wr_returning_cdemo_sk int,
wr_returning_hdemo_sk int,
wr_returning_addr_sk int,
wr_web_page_sk int,
wr_reason_sk int,
wr_order_number long,
wr_return_quantity int,
wr_return_amt decimal(7,2),
wr_return_tax decimal(7,2),
wr_return_amt_inc_tax decimal(7,2),
wr_fee decimal(7,2),
wr_return_ship_cost decimal(7,2),
wr_refunded_cash decimal(7,2),
wr_reversed_charge decimal(7,2),
wr_account_credit decimal(7,2),
wr_net_loss decimal(7,2)
) partitioned by (wr_returned_date_sk);
create table if not exists call_center
(
cc_call_center_sk int,
cc_call_center_id string,
cc_rec_start_date date,
cc_rec_end_date date,
cc_closed_date_sk int,
cc_open_date_sk int,
cc_name string,
cc_class string,
cc_employees int,
cc_sq_ft int,
cc_hours string,
cc_manager string,
cc_mkt_id int,
cc_mkt_class string,
cc_mkt_desc string,
cc_market_manager string,
cc_division int,
cc_division_name string,
cc_company int,
cc_company_name string,
cc_street_number string,
cc_street_name string,
cc_street_type string,
cc_suite_number string,
cc_city string,
cc_county string,
cc_state string,
cc_zip string,
cc_country string,
cc_gmt_offset decimal(5,2),
cc_tax_percentage decimal(5,2)
);
create table if not exists catalog_page (
cp_catalog_page_sk int,
cp_catalog_page_id string,
cp_start_date_sk int,
cp_end_date_sk int,
cp_department string,
cp_catalog_number int,
cp_catalog_page_number int,
cp_description string,
cp_type string) ;
create table if not exists customer (
c_customer_sk int,
c_customer_id string,
c_current_cdemo_sk int,
c_current_hdemo_sk int,
c_current_addr_sk int,
c_first_shipto_date_sk int,
c_first_sales_date_sk int,
c_salutation string,
c_first_name string,
c_last_name string,
c_preferred_cust_flag string,
c_birth_day int,
c_birth_month int,
c_birth_year int,
c_birth_country string,
c_login string,
c_email_address string,
c_last_review_date string) ;
create table if not exists customer_address (
ca_address_sk int,
ca_address_id string,
ca_street_number string,
ca_street_name string,
ca_street_type string,
ca_suite_number string,
ca_city string,
ca_county string,
ca_state string,
ca_zip string,
ca_country string,
ca_gmt_offset decimal(5,2),
ca_location_type string) ;
create table if not exists customer_demographics (
cd_demo_sk int,
cd_gender string,
cd_marital_status string,
cd_education_status string,
cd_purchase_estimate int,
cd_credit_rating string,
cd_dep_count int,
cd_dep_employed_count int,
cd_dep_college_count int) ;
create table if not exists date_dim (
d_date_sk int,
d_date_id string,
d_date date,
d_month_seq int,
d_week_seq int,
d_quarter_seq int,
d_year int,
d_dow int,
d_moy int,
d_dom int,
d_qoy int,
d_fy_year int,
d_fy_quarter_seq int,
d_fy_week_seq int,
d_day_name string,
d_quarter_name string,
d_holiday string,
d_weekend string,
d_following_holiday string,
d_first_dom int,
d_last_dom int,
d_same_day_ly int,
d_same_day_lq int,
d_current_day string,
d_current_week string,
d_current_month string,
d_current_quarter string,
d_current_year string) ;
create table if not exists household_demographics (
hd_demo_sk int,
hd_income_band_sk int,
hd_buy_potential string,
hd_dep_count int,
hd_vehicle_count int) ;
create table if not exists income_band (
ib_income_band_sk int,
ib_lower_bound int,
ib_upper_bound int) using parquet ;
create table if not exists item (
i_item_sk int,
i_item_id string,
i_rec_start_date date,
i_rec_end_date date,
i_item_desc string,
i_current_price decimal(7,2),
i_wholesale_cost decimal(7,2),
i_brand_id int,
i_brand string,
i_class_id int,
i_class string,
i_category_id int,
i_category string,
i_manufact_id int,
i_manufact string,
i_size string,
i_formulation string,
i_color string,
i_units string,
i_container string,
i_manager_id int,
i_product_name string) ;
create table if not exists promotion (
p_promo_sk int,
p_promo_id string,
p_start_date_sk int,
p_end_date_sk int,
p_item_sk int,
p_cost decimal(15,2),
p_response_target int,
p_promo_name string,
p_channel_dmail string,
p_channel_email string,
p_channel_catalog string,
p_channel_tv string,
p_channel_radio string,
p_channel_press string,
p_channel_event string,
p_channel_demo string,
p_channel_details string,
p_purpose string,
p_discount_active string) ;
create table if not exists reason (
r_reason_sk int,
r_reason_id string,
r_reason_desc string) ;
create table if not exists ship_mode (
sm_ship_mode_sk int,
sm_ship_mode_id string,
sm_type string,
sm_code string,
sm_carrier string,
sm_contract string) ;
create table if not exists store (
s_store_sk int,
s_store_id string,
s_rec_start_date date,
s_rec_end_date date,
s_closed_date_sk int,
s_store_name string,
s_number_employees int,
s_floor_space int,
s_hours string,
s_manager string,
s_market_id int,
s_geography_class string,
s_market_desc string,
s_market_manager string,
s_division_id int,
s_division_name string,
s_company_id int,
s_company_name string,
s_street_number string,
s_street_name string,
s_street_type string,
s_suite_number string,
s_city string,
s_county string,
s_state string,
s_zip string,
s_country string,
s_gmt_offset decimal(5,2),
s_tax_precentage decimal(5,2)) ;
create table if not exists time_dim (
t_time_sk int,
t_time_id string,
t_time int,
t_hour int,
t_minute int,
t_second int,
t_am_pm string,
t_shift string,
t_sub_shift string,
t_meal_time string) ;
create table if not exists warehouse (
w_warehouse_sk int,
w_warehouse_id string,
w_warehouse_name string,
w_warehouse_sq_ft int,
w_street_number string,
w_street_name string,
w_street_type string,
w_suite_number string,
w_city string,
w_county string,
w_state string,
w_zip string,
w_country string,
w_gmt_offset decimal(5,2)) ;
create table if not exists web_page (
wp_web_page_sk int,
wp_web_page_id string,
wp_rec_start_date date,
wp_rec_end_date date,
wp_creation_date_sk int,
wp_access_date_sk int,
wp_autogen_flag string,
wp_customer_sk int,
wp_url string,
wp_type string,
wp_char_count int,
wp_link_count int,
wp_image_count int,
wp_max_ad_count int) ;
create table if not exists web_site (
web_site_sk int,
web_site_id string,
web_rec_start_date date,
web_rec_end_date date,
web_name string,
web_open_date_sk int,
web_close_date_sk int,
web_class string,
web_manager string,
web_mkt_id int,
web_mkt_class string,
web_mkt_desc string,
web_market_manager string,
web_company_id int,
web_company_name string,
web_street_number string,
web_street_name string,
web_street_type string,
web_suite_number string,
web_city string,
web_county string,
web_state string,
web_zip string,
web_country string,
web_gmt_offset decimal(5,2),
web_tax_percentage decimal(5,2)) ;
analyze table call_center compute statistics for all columns;
analyze table catalog_page compute statistics for all columns;
analyze table catalog_returns compute statistics for all columns;
analyze table catalog_sales compute statistics for all columns;
analyze table customer compute statistics for all columns;
analyze table customer_address compute statistics for all columns;
analyze table customer_demographics compute statistics for all columns;
analyze table date_dim compute statistics for all columns;
analyze table household_demographics compute statistics for all columns;
analyze table income_band compute statistics for all columns;
analyze table inventory compute statistics for all columns;
analyze table item compute statistics for all columns;
analyze table promotion compute statistics for all columns;
analyze table reason compute statistics for all columns;
analyze table ship_mode compute statistics for all columns;
analyze table store compute statistics for all columns;
analyze table store_returns compute statistics for all columns;
analyze table store_sales compute statistics for all columns;
analyze table time_dim compute statistics for all columns;
analyze table warehouse compute statistics for all columns;
analyze table web_page compute statistics for all columns;
analyze table web_returns compute statistics for all columns;
analyze table web_sales compute statistics for all columns;
analyze table web_site compute statistics for all columns;
执行查询
TPC-DS 103个测试查询语句：TPC-DS-Query-SQL
测试结果
以下是云器Lakehouse和SparkSQL在103个查询上的性能测试结果，单位为秒(s)，数值越低表示性能越好。
| Query | 云器Lakehouse | Spark SQL | Spark vs. Lakehouse |
|---|
| query1 | 4.443 | 19.862 | 4.470402881 |
| query2 | 36.636 | 150.416 | 4.105688394 |
| query3 | 11.734 | 23.39 | 1.99335265 |
| query4 | 92.902 | 642.398 | 6.914791931 |
| query5 | 14.756 | 163.489 | 11.07949309 |
| query6 | 1.892 | 6.562 | 3.468287526 |
| query7 | 22.481 | 58.778 | 2.614563409 |
| query8 | 4.55 | 16.04 | 3.525274725 |
| query9 | 44.262 | 643.991 | 14.54952329 |
| query10 | 1.999 | 50.347 | 25.18609305 |
| query11 | 38.772 | 238.735 | 6.157407407 |
| query12 | 1.253 | 5.334 | 4.25698324 |
| query13 | 11.418 | 67.102 | 5.876861097 |
| query14a | 88.878 | 490.051 | 5.513749184 |
| query14b | 68.34 | 477.127 | 6.981665203 |
| query15 | 2.96 | 11.923 | 4.028040541 |
| query16 | 5.515 | 288.996 | 52.40181324 |
| query17 | 8.452 | 66.575 | 7.876833885 |
| query18 | 6.262 | 55.001 | 8.783296072 |
| query19 | 2.704 | 10.693 | 3.954511834 |
| query20 | 1.394 | 5.021 | 3.601865136 |
| query21 | 0.68 | 3.179 | 4.675 |
| query22 | 11.084 | 9.145 | 0.825063154 |
| query23a | 98.722 | 1393.112 | 14.11146452 |
| query23b | 95.845 | 1831.948 | 19.11365225 |
| query24a | 34.641 | 925.881 | 26.72789469 |
| query24b | 30.553 | 943.611 | 30.8843976 |
| query25 | 19.821 | 56.483 | 2.849654407 |
| query26 | 2.931 | 33.09 | 11.28966223 |
| query27 | 6.185 | 54.93 | 8.881164107 |
| query28 | 30.606 | 802.205 | 26.21071032 |
| query29 | 13.697 | 186.704 | 13.63101409 |
| query30 | 3.153 | 19.232 | 6.099587694 |
| query31 | 5.057 | 56.973 | 11.26616571 |
| query32 | 1.577 | 7.139 | 4.526949905 |
| query33 | 2.874 | 11.521 | 4.008698678 |
| query34 | 4.012 | 27.371 | 6.822283151 |
| query35 | 6.341 | 79.325 | 12.50985649 |
| query36 | 12.823 | 72.549 | 5.657724401 |
| query37 | 3.868 | 105.236 | 27.20682523 |
| query38 | 15.221 | 152.692 | 10.03166678 |
| query39a | 1.321 | 10.08 | 7.630582892 |
| query39b | 0.967 | 8.013 | 8.286452947 |
| query40 | 6.619 | 45.539 | 6.880042302 |
| query41 | 0.117 | 1.111 | 9.495726496 |
| query42 | 1.329 | 6.668 | 5.017306245 |
| query43 | 2.993 | 21.24 | 7.096558637 |
| query44 | 13.759 | 15.644 | 1.137001236 |
| query45 | 1.987 | 12.449 | 6.265223956 |
| query46 | 5.432 | 43.515 | 8.010861561 |
| query47 | 21.831 | 133.503 | 6.115294764 |
| query48 | 5.025 | 51.675 | 10.28358209 |
| query49 | 13.327 | 74.877 | 5.618443761 |
| query50 | 28.519 | 789.925 | 27.6982012 |
| query51 | 17.596 | 64.181 | 3.647476699 |
| query52 | 1.685 | 8.68 | 5.151335312 |
| query53 | 2.149 | 32.213 | 14.98976268 |
| query54 | 11.418 | 20.148 | 1.764582239 |
| query55 | 0.485 | 7.111 | 14.66185567 |
| query56 | 1.81 | 12.856 | 7.102762431 |
| query57 | 13.724 | 74.714 | 5.444039639 |
| query58 | 1.425 | 7.478 | 5.247719298 |
| query59 | 16.064 | 158.025 | 9.837213645 |
| query60 | 2.959 | 21.837 | 7.37985806 |
| query61 | 4.576 | 15.982 | 3.49256993 |
| query62 | 5.258 | 33.378 | 6.34804108 |
| query63 | 3.055 | 29.13 | 9.535188216 |
| query64 | 32.014 | 663.722 | 20.73224214 |
| query65 | 33.916 | 185.219 | 5.461109801 |
| query66 | 7.19 | 48.583 | 6.757023644 |
| query67 | 186.6 | 451.875 | 2.421623794 |
| query68 | 2.945 | 17.482 | 5.936162988 |
| query69 | 2.083 | 22.042 | 10.5818531 |
| query70 | 22.752 | 59.801 | 2.628384318 |
| query71 | 3.034 | 21.498 | 7.085695452 |
| query72 | 11.45 | 212.094 | 18.52349345 |
| query73 | 1.146 | 9.69 | 8.455497382 |
| query74 | 27.236 | 224.447 | 8.240820972 |
| query75 | 46.678 | 385.93 | 8.267920648 |
| query76 | 17.695 | 321.027 | 18.14224357 |
| query77 | 1.804 | 13.863 | 7.6845898 |
| query78 | 181.223 | 669.242 | 3.692919773 |
| query79 | 4.042 | 28.374 | 7.019792182 |
| query80 | 11.991 | 163.789 | 13.65932783 |
| query81 | 3.2 | 26.454 | 8.266875 |
| query82 | 4.572 | 208.444 | 45.59142607 |
| query83 | 0.795 | 6.253 | 7.865408805 |
| query84 | 4.277 | 23.8 | 5.564648118 |
| query85 | 6.826 | 46.928 | 6.874890126 |
| query86 | 7.527 | 32.703 | 4.344758868 |
| query87 | 15.751 | 159.444 | 10.12278585 |
| query88 | 52.074 | 801.005 | 15.38205246 |
| query89 | 3.389 | 34.134 | 10.07199764 |
| query90 | 4.48 | 66.709 | 14.89040179 |
| query91 | 0.527 | 6.78 | 12.86527514 |
| query92 | 1.621 | 5.989 | 3.694632943 |
| query93 | 0.03 | 0.866 | 28.86666667 |
| query94 | 10.33 | 164.88 | 15.96127783 |
| query95 | 49.464 | 381.528 | 7.713245997 |
| query96 | 11.947 | 99.414 | 8.321252197 |
| query97 | 30.497 | 178.751 | 5.861265042 |
| query98 | 2.005 | 9.795 | 4.885286783 |
| query99 | 9.352 | 62.952 | 6.731394354 |
| sum | 1869.187 | 17779.636 | 9.511962153 |
