Gunakan TPC-H untuk mengukur performa kueri OLAP, kueri titik key/value, dan pembaruan data di Hologres.
TPC-H
Berikut ini adalah kutipan dari spesifikasi TPC Benchmark™ H (TPC-H):
TPC-H adalah benchmark decision support yang terdiri dari serangkaian kueri ad-hoc berorientasi bisnis dan modifikasi data konkuren. Kueri serta data yang mengisi database dipilih karena relevansinya yang luas di berbagai industri. Benchmark ini merepresentasikan sistem decision support yang memproses volume data besar, menjalankan kueri dengan kompleksitas tinggi, serta memberikan jawaban atas pertanyaan bisnis kritis.
Unduh selengkapnya Spesifikasi TPC-H.
Implementasi TPC-H yang dijelaskan di sini didasarkan pada benchmark TPC-H tetapi tidak sepenuhnya sesuai. Oleh karena itu, hasil pengujian ini tidak dapat dibandingkan dengan hasil benchmark TPC-H resmi yang telah dipublikasikan.
Dataset
TPC-H adalah benchmark decision support yang dikembangkan oleh Transaction Processing Performance Council (TPC) dan secara luas digunakan untuk mengevaluasi performa kueri analitis.
TPC-H memodelkan gudang data untuk sistem penjualan berdasarkan lingkungan produksi nyata. Dataset ini mencakup delapan tabel dengan volume data yang dapat diskalakan dari 1 GB hingga 3 TB. Benchmark ini mencakup 22 kueri, dengan metrik utama berupa waktu respons kueri sebagaimana didefinisikan dalam benchmark TPC-H.
Kasus penggunaan
Rencana pengujian ini mencakup skenario berikut:
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Pengujian skenario kueri OLAP: Menjalankan 22 kueri dari benchmark TPC-H pada tabel berorientasi kolom.
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Pengujian skenario kueri titik key-value: Melakukan kueri titik yang difilter berdasarkan primary key pada tabel ORDERS berorientasi baris.
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Skenario pembaruan data: Mengevaluasi performa pembaruan data mesin OLAP pada tabel yang memiliki primary key.
Tool pembuat data TPC-H menggunakan scale factor (SF) untuk mengontrol ukuran dataset. SF sebesar 1 menghasilkan data sebesar 1 GB.
raw data volume di atas tidak termasuk indeks. Sediakan ruang disk tambahan sesuai kebutuhan.
Catatan
Untuk memastikan hasil pengujian yang konsisten, buat instans baru untuk setiap pengujian. Jangan gunakan instans yang telah diubah ukurannya.
Pengujian skenario kueri OLAP
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Siapkan lingkungan.
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Buat instans Hologres. Pengujian ini menggunakan instans dedicated berbayar sesuai penggunaan dengan 96 core dan memori 384 GB. Pilih sumber daya komputasi sesuai kebutuhan Anda.
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Buat instans ECS dengan spesifikasi berikut:
Parameter
Spesifikasi
Specification
ecs.g6.4xlarge
Image
Alibaba Cloud Linux 3.2104 LTS 64-bit
Data disk
ESSD. Kapasitasnya tergantung pada volume data uji Anda.
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Unduh dan konfigurasikan toolkit Hologres Benchmark.
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Login ke instans ECS. Hubungkan ke instans.
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Instal client psql.
yum update -y yum install postgresql-server -y yum install postgresql-contrib -y -
Unduh dan ekstrak toolkit Hologres Benchmark.
wget https://oss-tpch.oss-cn-hangzhou.aliyuncs.com/hologres_benchmark.tar.gz tar xvf hologres_benchmark.tar.gz -
Masuk ke direktori hologres_benchmark.
cd hologres_benchmark -
Jalankan perintah
vim group_vars/alluntuk mengonfigurasi parameter benchmark.# db config login_host: "" login_user: "" login_password: "" login_port: "" # benchmark run cluster: hologres cluster: "hologres" RUN_MODE: "HOTRUN" # benchmark config scale_factor: 1 work_dir_root: /your/working_dir/benchmark/workdirs dataset_generate_root_path: /your/working_dir/benchmark/datasetsDeskripsi parameter:
Tipe
Parameter
Deskripsi
Parameter koneksi layanan Hologres
login_host
Titik akhir VPC dari instans Hologres.
Untuk menemukan nilai ini, login ke Konsol Hologres dan buka halaman detail instans. Titik akhir VPC terdapat di kolom Domain Name pada bagian Network Information.
CatatanTitik akhir ini tidak mencakup nomor port. Contoh:
hgpostcn-cn-nwy364b5v009-cn-shanghai-vpc-st.hologres.aliyuncs.comlogin_port
Port untuk titik akhir VPC dari instans Hologres.
Untuk menemukan nilai ini, login ke Konsol Hologres dan buka halaman detail instans. Port terdapat di kolom Domain Name pada bagian Network Information.
login_user
ID AccessKey Anda.
Buka Manajemen AccessKey untuk mendapatkan ID AccessKey Anda.
login_password
Rahasia AccessKey Anda.
Parameter konfigurasi benchmark
scale_factor
Faktor skala dataset, yang mengontrol ukuran data yang dihasilkan dalam satuan GB. Nilai default-nya adalah 1.
work_dir_root
Direktori kerja root. Direktori ini menyimpan data TPC-H, seperti pernyataan pembuatan tabel dan pernyataan SQL yang dieksekusi. Nilai default-nya adalah
/your/working_dir/benchmark/workdirs.dataset_generate_root_path
Jalur penyimpanan untuk dataset uji yang dihasilkan. Nilai default-nya adalah
/your/working_dir/benchmark/datasets.
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Jalankan perintah berikut untuk memulai pengujian TPC-H otomatis end-to-end.
Pengujian ini menghasilkan data, membuat database uji (misalnya, tpc_h_sf1000), membuat tabel, dan mengimpor data.
bin/run_tpch.shAtau, Anda dapat menjalankan perintah berikut hanya untuk menjalankan pengujian kueri TPC-H.
bin/run_tpch.sh query -
Lihat hasil pengujian.
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Ikhtisar hasil pengujian
Perintah
bin/run_tpch.shmenghasilkan output seperti berikut:TASK [tpc_h : debug] ************************************************************************************************** skipping: [worker-1] ok: [master] => { "command_output.stdout_lines": [ "[info] 2024-06-28 14:46:09.768 | Run sql queries started.", "[info] 2024-06-28 14:46:09.947 | Run q10.sql started.", "[info] 2024-06-28 14:46:10.088 | Run q10.sql finished. Time taken: 0:00:00, 138 ms", "[info] 2024-06-28 14:46:10.239 | Run q11.sql started.", "[info] 2024-06-28 14:46:10.396 | Run q11.sql finished. Time taken: 0:00:00, 154 ms", "[info] 2024-06-28 14:46:10.505 | Run q12.sql started.", "[info] 2024-06-28 14:46:10.592 | Run q12.sql finished. Time taken: 0:00:00, 85 ms", "[info] 2024-06-28 14:46:10.703 | Run q13.sql started.", "[info] 2024-06-28 14:46:10.793 | Run q13.sql finished. Time taken: 0:00:00, 88 ms", "[info] 2024-06-28 14:46:10.883 | Run q14.sql started.", "[info] 2024-06-28 14:46:10.981 | Run q14.sql finished. Time taken: 0:00:00, 95 ms", "[info] 2024-06-28 14:46:11.132 | Run q15.sql started.", "[info] 2024-06-28 14:46:11.266 | Run q15.sql finished. Time taken: 0:00:00, 131 ms", "[info] 2024-06-28 14:46:11.441 | Run q16.sql started.", "[info] 2024-06-28 14:46:11.609 | Run q16.sql finished. Time taken: 0:00:00, 165 ms", "[info] 2024-06-28 14:46:11.728 | Run q17.sql started.", "[info] 2024-06-28 14:46:11.818 | Run q17.sql finished. Time taken: 0:00:00, 88 ms", "[info] 2024-06-28 14:46:12.017 | Run q18.sql started.", "[info] 2024-06-28 14:46:12.184 | Run q18.sql finished. Time taken: 0:00:00, 164 ms", "[info] 2024-06-28 14:46:12.287 | Run q19.sql started.", "[info] 2024-06-28 14:46:12.388 | Run q19.sql finished. Time taken: 0:00:00, 98 ms", "[info] 2024-06-28 14:46:12.503 | Run q1.sql started.", "[info] 2024-06-28 14:46:12.597 | Run q1.sql finished. Time taken: 0:00:00, 93 ms", "[info] 2024-06-28 14:46:12.732 | Run q20.sql started.", "[info] 2024-06-28 14:46:12.888 | Run q20.sql finished. Time taken: 0:00:00, 154 ms", "[info] 2024-06-28 14:46:13.184 | Run q21.sql started.", "[info] 2024-06-28 14:46:13.456 | Run q21.sql finished. Time taken: 0:00:00, 269 ms", "[info] 2024-06-28 14:46:13.558 | Run q22.sql started.", "[info] 2024-06-28 14:46:13.657 | Run q22.sql finished. Time taken: 0:00:00, 97 ms", "[info] 2024-06-28 14:46:13.796 | Run q2.sql started.", "[info] 2024-06-28 14:46:13.935 | Run q2.sql finished. Time taken: 0:00:00, 136 ms", "[info] 2024-06-28 14:46:14.051 | Run q3.sql started.", "[info] 2024-06-28 14:46:14.155 | Run q3.sql finished. Time taken: 0:00:00, 101 ms", "[info] 2024-06-28 14:46:14.255 | Run q4.sql started.", "[info] 2024-06-28 14:46:14.341 | Run q4.sql finished. Time taken: 0:00:00, 83 ms", "[info] 2024-06-28 14:46:14.567 | Run q5.sql started.", "[info] 2024-06-28 14:46:14.799 | Run q5.sql finished. Time taken: 0:00:00, 230 ms", "[info] 2024-06-28 14:46:14.881 | Run q6.sql started.", "[info] 2024-06-28 14:46:14.950 | Run q6.sql finished. Time taken: 0:00:00, 67 ms", "[info] 2024-06-28 14:46:15.138 | Run q7.sql started.", "[info] 2024-06-28 14:46:15.320 | Run q7.sql finished. Time taken: 0:00:00, 180 ms", "[info] 2024-06-28 14:46:15.572 | Run q8.sql started.", "[info] 2024-06-28 14:46:15.831 | Run q8.sql finished. Time taken: 0:00:00, 256 ms", "[info] 2024-06-28 14:46:16.081 | Run q9.sql started.", "[info] 2024-06-28 14:46:16.322 | Run q9.sql finished. Time taken: 0:00:00, 238 ms", "[info] 2024-06-28 14:46:16.325 | ----------- HOT RUN finished. Time taken: 3255 mill_sec -----------------" ] } skipping: [worker-2] skipping: [worker-3] skipping: [worker-4] TASK [tpc_h : clear Env] ********************************************************************************************** skipping: [worker-1] skipping: [worker-2] skipping: [worker-3] skipping: [worker-4] ok: [master] TASK [tpc_h : debug] ************************************************************************************************** ok: [master] => { "work_dir": "/your/working_dir/benchmark/workdirs/tpc_h/sf1" } skipping: [worker-1] skipping: [worker-2] skipping: [worker-3] skipping: [worker-4] -
Detail hasil pengujian
Perintah
bin/run_tpch.shmembuat direktori kerja dan menampilkan jalurnya sebagai<work_dir>. Masuk ke direktori ini untuk melihat pernyataan kueri, pernyataan pembuatan tabel, dan log eksekusi.TASK [tpc_h : debug] ************************************************************ ok: [master] => { "work_dir": "/your/working_dir/benchmark/workdirs/tpc_h/sf1" } skipping: [worker-1] skipping: [worker-2] skipping: [worker-3] skipping: [worker-4]Jalankan
cd <work_dir>/logsuntuk masuk ke direktori logs dan melihat hasil pengujian beserta output SQL lengkapnya.Struktur direktori
<work_dir>adalah sebagai berikut:working_dir/ `-- benchmark |-- datasets | `-- tpc_h | `-- sf1 | |-- worker-1 | | |-- customer.tbl | | `-- lineitem.tbl | |-- worker-2 | | |-- orders.tbl | | `-- supplier.tbl | |-- worker-3 | | |-- nation.tbl | | `-- partsupp.tbl | `-- worker-4 | |-- part.tbl | `-- region.tbl `-- workdirs `-- tpc_h `-- sf1 |-- config |-- hologres | |-- logs | | |-- q10.sql.err | | |-- q10.sql.out | | |-- q11.sql.err | | |-- q11.sql.out | | |-- q12.sql.err | | |-- q12.sql.out | | |-- q13.sql.err | | |-- q13.sql.out | | |-- q14.sql.err | | |-- q14.sql.out | | |-- q15.sql.err | | |-- q15.sql.out | | |-- q16.sql.err | | |-- q16.sql.out | | |-- q17.sql.err | | |-- q17.sql.out | | |-- q18.sql.err | | |-- q18.sql.out | | |-- q19.sql.err | | |-- q19.sql.out | | |-- q1.sql.err | | |-- q1.sql.out | | |-- q20.sql.err | | |-- q20.sql.out | | |-- q21.sql.err | | |-- q21.sql.out | | |-- q22.sql.err | | |-- q22.sql.out | | |-- q2.sql.err | | |-- q2.sql.out | | |-- q3.sql.err | | |-- q3.sql.out | | |-- q4.sql.err | | |-- q4.sql.out | | |-- q5.sql.err | | |-- q5.sql.out | | |-- q6.sql.err | | |-- q6.sql.out | | |-- q7.sql.err | | |-- q7.sql.out | | |-- q8.sql.err | | |-- q8.sql.out | | |-- q9.sql.err | | |-- q9.sql.out | | `-- run.log | `-- logs-20240628144609 | |-- q10.sql.err | |-- q10.sql.out | |-- q11.sql.err | |-- q11.sql.out | |-- q12.sql.err | |-- q12.sql.out | |-- q13.sql.err | |-- q13.sql.out | |-- q14.sql.err | |-- q14.sql.out | |-- q15.sql.err | |-- q15.sql.out | |-- q16.sql.err | |-- q16.sql.out | |-- q17.sql.err | |-- q17.sql.out | |-- q18.sql.err | |-- q18.sql.out | |-- q19.sql.err | |-- q19.sql.out | |-- q1.sql.err | |-- q1.sql.out | |-- q20.sql.err | |-- q20.sql.out | |-- q21.sql.err | |-- q21.sql.out | |-- q22.sql.err | |-- q22.sql.out | |-- q2.sql.err | |-- q2.sql.out | |-- q3.sql.err | |-- q3.sql.out | |-- q4.sql.err | |-- q4.sql.out | |-- q5.sql.err | |-- q5.sql.out | |-- q6.sql.err | |-- q6.sql.out | |-- q7.sql.err | |-- q7.sql.out | |-- q8.sql.err | |-- q8.sql.out | |-- q9.sql.err | |-- q9.sql.out | `-- run.log |-- queries | |-- ddl | | |-- hologres_analyze_tables.sql | | `-- hologres_create_tables.sql | |-- q10.sql | |-- q11.sql | |-- q12.sql | |-- q13.sql | |-- q14.sql | |-- q15.sql | |-- q16.sql | |-- q17.sql | |-- q18.sql | |-- q19.sql | |-- q1.sql | |-- q20.sql | |-- q21.sql | |-- q22.sql | |-- q2.sql | |-- q3.sql | |-- q4.sql | |-- q5.sql | |-- q6.sql | |-- q7.sql | |-- q8.sql | `-- q9.sql |-- run_hologres.sh |-- run_mysql.sh |-- run.sh `-- tpch_tools |-- dbgen |-- qgen `-- resouces |-- dists.dss `-- queries |-- 10.sql |-- 11.sql |-- 12.sql |-- 13.sql |-- 14.sql |-- 15.sql |-- 16.sql |-- 17.sql |-- 18.sql |-- 19.sql |-- 1.sql |-- 20.sql |-- 21.sql |-- 22.sql |-- 2.sql |-- 3.sql |-- 4.sql |-- 5.sql |-- 6.sql |-- 7.sql |-- 8.sql `-- 9.sql
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Pengujian kueri titik key/value
Pengujian ini menggunakan database hologres_tpch dan tabel orders dari pengujian kueri OLAP.
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Buat tabel
Kueri titik key/value memerlukan tabel berorientasi baris. Hubungkan ke Hologres dengan client psql dan jalankan pernyataan berikut untuk membuat tabel
orders_row.CatatanUntuk menghubungkan dengan client psql, ikuti langkah-langkah dalam Hubungkan ke Hologres untuk Pengembangan.
DROP TABLE IF EXISTS public.orders_row; BEGIN; CREATE TABLE public.orders_row( O_ORDERKEY BIGINT NOT NULL PRIMARY KEY ,O_CUSTKEY INT NOT NULL ,O_ORDERSTATUS TEXT NOT NULL ,O_TOTALPRICE DECIMAL(15,2) NOT NULL ,O_ORDERDATE TIMESTAMPTZ NOT NULL ,O_ORDERPRIORITY TEXT NOT NULL ,O_CLERK TEXT NOT NULL ,O_SHIPPRIORITY INT NOT NULL ,O_COMMENT TEXT NOT NULL ); CALL SET_TABLE_PROPERTY('public.orders_row', 'orientation', 'row'); CALL SET_TABLE_PROPERTY('public.orders_row', 'clustering_key', 'o_orderkey'); CALL SET_TABLE_PROPERTY('public.orders_row', 'distribution_key', 'o_orderkey'); COMMIT; -
Impor data
Gunakan pernyataan INSERT INTO berikut untuk mengimpor data dari tabel
ordersdalam dataset TPC-H ke tabelorders_row.CatatanHologres V2.1.17 dan versi lebih baru mendukung Serverless Computing. Untuk skenario seperti impor data offline skala besar, pekerjaan ETL besar, dan kueri volume tinggi pada tabel eksternal, Anda dapat menggunakan Serverless Computing untuk menjalankan tugas-tugas tersebut. Fitur ini menggunakan sumber daya serverless tambahan alih-alih sumber daya instans Anda, sehingga meningkatkan stabilitas instans dan mengurangi kemungkinan error kehabisan memori (OOM). Anda tidak perlu menyediakan sumber daya komputasi tambahan untuk instans Anda, dan hanya dikenakan biaya untuk tugas yang dijalankan. Untuk informasi lebih lanjut tentang Serverless Computing, lihat Serverless Computing. Untuk petunjuk cara menggunakan Serverless Computing, lihat Gunakan Serverless Computing.
-- (Opsional) Disarankan menggunakan Serverless Computing untuk impor offline massal dan pekerjaan ETL. SET hg_computing_resource = 'serverless'; INSERT INTO public.orders_row SELECT * FROM public.orders; -- Setel ulang konfigurasi agar pernyataan SQL berikutnya tidak menggunakan sumber daya serverless. RESET hg_computing_resource; -
Jalankan kueri
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Hasilkan pernyataan kueri.
Kueri titik key/value terdiri dari dua jenis:
Jenis
Pernyataan kueri
Deskripsi
single-value point query
SELECT column_a ,column_b ,... ,column_x FROM table_x WHERE pk = value_x ;Kueri ini melakukan filter berdasarkan satu nilai dalam klausa
WHERE.multi-value point query
SELECT column_a ,column_b ,... ,column_x FROM table_x WHERE pk IN ( value_a, value_b,..., value_x ) ;Kueri ini melakukan filter berdasarkan beberapa nilai dalam klausa
WHERE.Jalankan skrip berikut untuk menghasilkan pernyataan SQL yang diperlukan.
rm -rf kv_query mkdir kv_query cd kv_query echo " \set column_values random(1,99999999) select O_ORDERKEY,O_CUSTKEY,O_ORDERSTATUS,O_TOTALPRICE,O_ORDERDATE,O_ORDERPRIORITY,O_CLERK,O_SHIPPRIORITY,O_COMMENT from public.orders_row WHERE o_orderkey =:column_values; " >> kv_query_single.sql echo " \set column_values1 random(1,99999999) \set column_values2 random(1,99999999) \set column_values3 random(1,99999999) \set column_values4 random(1,99999999) \set column_values5 random(1,99999999) \set column_values6 random(1,99999999) \set column_values7 random(1,99999999) \set column_values8 random(1,99999999) \set column_values9 random(1,99999999) select O_ORDERKEY,O_CUSTKEY,O_ORDERSTATUS,O_TOTALPRICE,O_ORDERDATE,O_ORDERPRIORITY,O_CLERK,O_SHIPPRIORITY,O_COMMENT from public.orders_row WHERE o_orderkey in(:column_values1,:column_values2,:column_values3,:column_values4,:column_values5,:column_values6,:column_values7,:column_values8,:column_values9); " >> kv_query_in.sqlSkrip ini menghasilkan dua file SQL:
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kv_query_single.sql: Berisi SQL untuk kueri titik single-value. -
kv_query_in.sql: Berisi SQL untuk kueri titik multi-value. Skrip ini menghasilkan pernyataan SQL yang melakukan filter berdasarkan sembilan nilai acak.
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Untuk mengumpulkan statistik kueri, gunakan tool pgbench. Instal dengan menjalankan perintah berikut.
yum install postgresql-contrib -yInstal pgbench versi 13 atau lebih baru. Jika pgbench sudah terinstal, pastikan versinya 9.6 atau lebih baru. Untuk memeriksa versi:
pgbench --version -
Jalankan pernyataan pengujian.
CatatanJalankan perintah berikut di direktori tempat Anda menghasilkan pernyataan kueri.
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Untuk skenario kueri titik single-value, gunakan pgbench untuk menjalankan uji stres.
PGUSER=<AccessKey ID> PGPASSWORD=<AccessKey Secret> PGDATABASE=<database> pgbench -h <endpoint> -p <port> -c <Client_Num> -T <Query_Seconds> -M prepared -n -f kv_query_single.sql -
Untuk skenario kueri titik multi-value, gunakan pgbench untuk menjalankan uji stres.
PGUSER=<AccessKey ID> PGPASSWORD=<AccessKey Secret> PGDATABASE=<database> pgbench -h <endpoint> -p <port> -c <Client_Num> -T <Query_Seconds> -M prepared -n -f kv_query_in.sql
Deskripsi parameter:
Parameter
Deskripsi
AccessKey ID
ID AccessKey Akun Alibaba Cloud Anda.
Buka halaman Manajemen AccessKey untuk mendapatkan ID AccessKey.
AccessKey Secret
Rahasia AccessKey Akun Alibaba Cloud Anda.
Buka halaman Manajemen AccessKey untuk mendapatkan Rahasia AccessKey.
database
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Nama database Hologres.
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Setelah membuat instans Hologres, sistem secara otomatis membuat database bernama
postgres. -
Anda dapat menggunakan database
postgresdefault, tetapi sumber dayanya terbatas. Untuk beban kerja produksi, Buat database.
endpoint
Titik akhir instans Hologres.
Buka halaman Detail Instans di Konsol Hologres dan dapatkan titik akhir dari bagian Network Information.
port
Port instans Hologres.
Buka halaman Instance Details di Konsol Hologres untuk mendapatkan port.
Client_Num
Jumlah client konkuren.
Karena pengujian ini mengukur performa kueri alih-alih konkurensi, atur parameter ini menjadi 1.
Query_Seconds
Durasi total uji stres, dalam detik. Misalnya, Anda dapat mengatur parameter ini menjadi 300.
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Pembaruan data
Skenario ini menguji performa mesin OLAP pada pembaruan full-row yang dipicu oleh konflik primary key.
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Hasilkan file kueri.
echo " \set O_ORDERKEY random(1,99999999) INSERT INTO public.orders_row(o_orderkey,o_custkey,o_orderstatus,o_totalprice,o_orderdate,o_orderpriority,o_clerk,o_shippriority,o_comment) VALUES (:O_ORDERKEY,1,'demo',1.1,'2021-01-01','demo','demo',1,'demo') on conflict(o_orderkey) do update set (o_orderkey,o_custkey,o_orderstatus,o_totalprice,o_orderdate,o_orderpriority,o_clerk,o_shippriority,o_comment)= ROW(excluded.*); " > /root/insert_on_conflict.sql -
Insert dan update data. Parameter.
PGUSER=<AccessKey_ID> PGPASSWORD=<AccessKey_Secret> PGDATABASE=<Database> pgbench -h <Endpoint> -p <Port> -c <Client_Num> -T <Query_Seconds> -M prepared -n -f /root/insert_on_conflict.sql -
Contoh hasil:
transaction type: Custom query scaling factor: 1 query mode: prepared number of clients: 249 number of threads: 1 duration: 60 s number of transactions actually processed: 1923038 tps = 32005.850214 (including connections establishing) tps = 36403.145722 (excluding connections establishing)
Penulisan real-time Flink
Kasus penggunaan ini mengevaluasi performa penulisan data real-time.
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DDL Hologres
Tabel Hologres yang digunakan dalam kasus ini memiliki 10 kolom, dan kolom
keyberfungsi sebagai primary key.DROP TABLE IF EXISTS flink_insert; BEGIN ; CREATE TABLE IF NOT EXISTS flink_insert( key INT PRIMARY KEY ,value1 TEXT ,value2 TEXT ,value3 TEXT ,value4 TEXT ,value5 TEXT ,value6 TEXT ,value7 TEXT ,value8 TEXT ,value9 TEXT ); CALL SET_TABLE_PROPERTY('flink_insert', 'orientation', 'row'); CALL SET_TABLE_PROPERTY('flink_insert', 'clustering_key', 'key'); CALL SET_TABLE_PROPERTY('flink_insert', 'distribution_key', 'key'); COMMIT; -
Skrip pekerjaan Flink
Skrip ini menggunakan generator data acak bawaan dari Realtime Compute for Apache Flink untuk menulis data ke Hologres. Skrip ini memperbarui seluruh baris saat terjadi konflik primary key, dan setiap baris berukuran lebih dari 512 B.
CREATE TEMPORARY TABLE flink_case_1_source ( key INT, value1 VARCHAR, value2 VARCHAR, value3 VARCHAR, value4 VARCHAR, value5 VARCHAR, value6 VARCHAR, value7 VARCHAR, value8 VARCHAR, value9 VARCHAR ) WITH ( 'connector' = 'datagen', -- optional options -- 'rows-per-second' = '1000000000', 'fields.key.min'='1', 'fields.key.max'='2147483647', 'fields.value1.length' = '57', 'fields.value2.length' = '57', 'fields.value3.length' = '57', 'fields.value4.length' = '57', 'fields.value5.length' = '57', 'fields.value6.length' = '57', 'fields.value7.length' = '57', 'fields.value8.length' = '57', 'fields.value9.length' = '57' ); -- Create a Hologres sink table. CREATE TEMPORARY TABLE flink_case_1_sink ( key INT, value1 VARCHAR, value2 VARCHAR, value3 VARCHAR, value4 VARCHAR, value5 VARCHAR, value6 VARCHAR, value7 VARCHAR, value8 VARCHAR, value9 VARCHAR ) WITH ( 'connector' = 'hologres', 'dbname'='<yourDbname>', -- The name of the Hologres database. 'tablename'='<yourTablename>', -- The name of the Hologres table that receives the data. 'username'='<yourUsername>', -- Your AccessKey ID. 'password'='<yourPassword>', -- Your AccessKey Secret. 'endpoint'='<yourEndpoint>', -- The VPC endpoint of your Hologres instance. 'connectionSize' = '10', -- The default value is 3. 'jdbcWriteBatchSize' = '1024', -- The default value is 256. 'jdbcWriteBatchByteSize' = '2147483647', -- The default value is 20971520. 'mutatetype'='insertorreplace' -- Inserts a new row or replaces an existing row on conflict. ); -- Write data from the source to the sink. insert into flink_case_1_sink select key, value1, value2, value3, value4, value5, value6, value7, value8, value9 from flink_case_1_source ; -
Contoh hasil
Di Konsol Hologres, buka halaman Monitoring Information. Pada grafik Query Latency (ms), metrik select_avg mencapai puncak sekitar 400 ms. Pada grafik Real-time Import RPS (records/second), metrik sdk berfluktuasi antara 200.000 hingga 400.000 records/second.
22 pernyataan kueri TPC-H
Klik kueri dalam tabel untuk melihat pernyataan SQL-nya.
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22 pernyataan kueri TPC-H |
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Q1
select l_returnflag, l_linestatus, sum(l_quantity) as sum_qty, sum(l_extendedprice) as sum_base_price, sum(l_extendedprice * (1 - l_discount)) as sum_disc_price, sum(l_extendedprice * (1 - l_discount) * (1 + l_tax)) as sum_charge, avg(l_quantity) as avg_qty, avg(l_extendedprice) as avg_price, avg(l_discount) as avg_disc, count(*) as count_order from lineitem where l_shipdate <= date '1998-12-01' - interval '120' day group by l_returnflag, l_linestatus order by l_returnflag, l_linestatus; -
Q2
select s_acctbal, s_name, n_name, p_partkey, p_mfgr, s_address, s_phone, s_comment from part, supplier, partsupp, nation, region where p_partkey = ps_partkey and s_suppkey = ps_suppkey and p_size = 48 and p_type like '%STEEL' and s_nationkey = n_nationkey and n_regionkey = r_regionkey and r_name = 'EUROPE' and ps_supplycost = ( select min(ps_supplycost) from partsupp, supplier, nation, region where p_partkey = ps_partkey and s_suppkey = ps_suppkey and s_nationkey = n_nationkey and n_regionkey = r_regionkey and r_name = 'EUROPE' ) order by s_acctbal desc, n_name, s_name, p_partkey limit 100; -
Q3
select l_orderkey, sum(l_extendedprice * (1 - l_discount)) as revenue, o_orderdate, o_shippriority from customer, orders, lineitem where c_mktsegment = 'MACHINERY' and c_custkey = o_custkey and l_orderkey = o_orderkey and o_orderdate < date '1995-03-23' and l_shipdate > date '1995-03-23' group by l_orderkey, o_orderdate, o_shippriority order by revenue desc, o_orderdate limit 10; -
Q4
select o_orderpriority, count(*) as order_count from orders where o_orderdate >= date '1996-07-01' and o_orderdate < date '1996-07-01' + interval '3' month and exists ( select * from lineitem where l_orderkey = o_orderkey and l_commitdate < l_receiptdate ) group by o_orderpriority order by o_orderpriority; -
Q5
select n_name, sum(l_extendedprice * (1 - l_discount)) as revenue from customer, orders, lineitem, supplier, nation, region where c_custkey = o_custkey and l_orderkey = o_orderkey and l_suppkey = s_suppkey and c_nationkey = s_nationkey and s_nationkey = n_nationkey and n_regionkey = r_regionkey and r_name = 'EUROPE' and o_orderdate >= date '1996-01-01' and o_orderdate < date '1996-01-01' + interval '1' year group by n_name order by revenue desc; -
Q6
select sum(l_extendedprice * l_discount) as revenue from lineitem where l_shipdate >= date '1996-01-01' and l_shipdate < date '1996-01-01' + interval '1' year and l_discount between 0.02 - 0.01 and 0.02 + 0.01 and l_quantity < 24; -
Q7
select supp_nation, cust_nation, l_year, sum(volume) as revenue from ( select n1.n_name as supp_nation, n2.n_name as cust_nation, extract(year from l_shipdate) as l_year, l_extendedprice * (1 - l_discount) as volume from supplier, lineitem, orders, customer, nation n1, nation n2 where s_suppkey = l_suppkey and o_orderkey = l_orderkey and c_custkey = o_custkey and s_nationkey = n1.n_nationkey and c_nationkey = n2.n_nationkey and ( (n1.n_name = 'CANADA' and n2.n_name = 'BRAZIL') or (n1.n_name = 'BRAZIL' and n2.n_name = 'CANADA') ) and l_shipdate between date '1995-01-01' and date '1996-12-31' ) as shipping group by supp_nation, cust_nation, l_year order by supp_nation, cust_nation, l_year; -
Q8
select o_year, sum(case when nation = 'BRAZIL' then volume else 0 end) / sum(volume) as mkt_share from ( select extract(year from o_orderdate) as o_year, l_extendedprice * (1 - l_discount) as volume, n2.n_name as nation from part, supplier, lineitem, orders, customer, nation n1, nation n2, region where p_partkey = l_partkey and s_suppkey = l_suppkey and l_orderkey = o_orderkey and o_custkey = c_custkey and c_nationkey = n1.n_nationkey and n1.n_regionkey = r_regionkey and r_name = 'AMERICA' and s_nationkey = n2.n_nationkey and o_orderdate between date '1995-01-01' and date '1996-12-31' and p_type = 'LARGE ANODIZED COPPER' ) as all_nations group by o_year order by o_year; -
Q9
select nation, o_year, sum(amount) as sum_profit from ( select n_name as nation, extract(year from o_orderdate) as o_year, l_extendedprice * (1 - l_discount) - ps_supplycost * l_quantity as amount from part, supplier, lineitem, partsupp, orders, nation where s_suppkey = l_suppkey and ps_suppkey = l_suppkey and ps_partkey = l_partkey and p_partkey = l_partkey and o_orderkey = l_orderkey and s_nationkey = n_nationkey and p_name like '%maroon%' ) as profit group by nation, o_year order by nation, o_year desc; -
Q10
select c_custkey, c_name, sum(l_extendedprice * (1 - l_discount)) as revenue, c_acctbal, n_name, c_address, c_phone, c_comment from customer, orders, lineitem, nation where c_custkey = o_custkey and l_orderkey = o_orderkey and o_orderdate >= date '1993-02-01' and o_orderdate < date '1993-02-01' + interval '3' month and l_returnflag = 'R' and c_nationkey = n_nationkey group by c_custkey, c_name, c_acctbal, c_phone, n_name, c_address, c_comment order by revenue desc limit 20; -
Q11
select ps_partkey, sum(ps_supplycost * ps_availqty) as value from partsupp, supplier, nation where ps_suppkey = s_suppkey and s_nationkey = n_nationkey and n_name = 'EGYPT' group by ps_partkey having sum(ps_supplycost * ps_availqty) > ( select sum(ps_supplycost * ps_availqty) * 0.0001000000 from partsupp, supplier, nation where ps_suppkey = s_suppkey and s_nationkey = n_nationkey and n_name = 'EGYPT' ) order by value desc; -
Q12
select l_shipmode, sum(case when o_orderpriority = '1-URGENT' or o_orderpriority = '2-HIGH' then 1 else 0 end) as high_line_count, sum(case when o_orderpriority <> '1-URGENT' and o_orderpriority <> '2-HIGH' then 1 else 0 end) as low_line_count from orders, lineitem where o_orderkey = l_orderkey and l_shipmode in ('FOB', 'AIR') and l_commitdate < l_receiptdate and l_shipdate < l_commitdate and l_receiptdate >= date '1997-01-01' and l_receiptdate < date '1997-01-01' + interval '1' year group by l_shipmode order by l_shipmode; -
Q13
select c_count, count(*) as custdist from ( select c_custkey, count(o_orderkey) as c_count from customer left outer join orders on c_custkey = o_custkey and o_comment not like '%special%deposits%' group by c_custkey ) c_orders group by c_count order by custdist desc, c_count desc; -
Q14
select 100.00 * sum(case when p_type like 'PROMO%' then l_extendedprice * (1 - l_discount) else 0 end) / sum(l_extendedprice * (1 - l_discount)) as promo_revenue from lineitem, part where l_partkey = p_partkey and l_shipdate >= date '1997-06-01' and l_shipdate < date '1997-06-01' + interval '1' month; -
Q15
with revenue0(SUPPLIER_NO, TOTAL_REVENUE) as ( select l_suppkey, sum(l_extendedprice * (1 - l_discount)) from lineitem where l_shipdate >= date '1995-02-01' and l_shipdate < date '1995-02-01' + interval '3' month group by l_suppkey ) select s_suppkey, s_name, s_address, s_phone, total_revenue from supplier, revenue0 where s_suppkey = supplier_no and total_revenue = ( select max(total_revenue) from revenue0 ) order by s_suppkey; -
Q16
select p_brand, p_type, p_size, count(distinct ps_suppkey) as supplier_cnt from partsupp, part where p_partkey = ps_partkey and p_brand <> 'Brand#45' and p_type not like 'SMALL ANODIZED%' and p_size in (47, 15, 37, 30, 46, 16, 18, 6) and ps_suppkey not in ( select s_suppkey from supplier where s_comment like '%Customer%Complaints%' ) group by p_brand, p_type, p_size order by supplier_cnt desc, p_brand, p_type, p_size; -
Q17
select sum(l_extendedprice) / 7.0 as avg_yearly from lineitem, part where p_partkey = l_partkey and p_brand = 'Brand#51' and p_container = 'WRAP PACK' and l_quantity < ( select 0.2 * avg(l_quantity) from lineitem where l_partkey = p_partkey ); -
Q18
select c_name, c_custkey, o_orderkey, o_orderdate, o_totalprice, sum(l_quantity) from customer, orders, lineitem where o_orderkey in ( select l_orderkey from lineitem group by l_orderkey having sum(l_quantity) > 312 ) and c_custkey = o_custkey and o_orderkey = l_orderkey group by c_name, c_custkey, o_orderkey, o_orderdate, o_totalprice order by o_totalprice desc, o_orderdate limit 100; -
Q19
select sum(l_extendedprice* (1 - l_discount)) as revenue from lineitem, part where ( p_partkey = l_partkey and p_brand = 'Brand#52' and p_container in ('SM CASE', 'SM BOX', 'SM PACK', 'SM PKG') and l_quantity >= 3 and l_quantity <= 3 + 10 and p_size between 1 and 5 and l_shipmode in ('AIR', 'AIR REG') and l_shipinstruct = 'DELIVER IN PERSON' ) or ( p_partkey = l_partkey and p_brand = 'Brand#43' and p_container in ('MED BAG', 'MED BOX', 'MED PKG', 'MED PACK') and l_quantity >= 12 and l_quantity <= 12 + 10 and p_size between 1 and 10 and l_shipmode in ('AIR', 'AIR REG') and l_shipinstruct = 'DELIVER IN PERSON' ) or ( p_partkey = l_partkey and p_brand = 'Brand#52' and p_container in ('LG CASE', 'LG BOX', 'LG PACK', 'LG PKG') and l_quantity >= 21 and l_quantity <= 21 + 10 and p_size between 1 and 15 and l_shipmode in ('AIR', 'AIR REG') and l_shipinstruct = 'DELIVER IN PERSON' ); -
Q20
select s_name, s_address from supplier, nation where s_suppkey in ( select ps_suppkey from partsupp where ps_partkey in ( select p_partkey from part where p_name like 'drab%' ) and ps_availqty > ( select 0.5 * sum(l_quantity) from lineitem where l_partkey = ps_partkey and l_suppkey = ps_suppkey and l_shipdate >= date '1996-01-01' and l_shipdate < date '1996-01-01' + interval '1' year ) ) and s_nationkey = n_nationkey and n_name = 'KENYA' order by s_name; -
Q21
select s_name, count(*) as numwait from supplier, lineitem l1, orders, nation where s_suppkey = l1.l_suppkey and o_orderkey = l1.l_orderkey and o_orderstatus = 'F' and l1.l_receiptdate > l1.l_commitdate and exists ( select * from lineitem l2 where l2.l_orderkey = l1.l_orderkey and l2.l_suppkey <> l1.l_suppkey ) and not exists ( select * from lineitem l3 where l3.l_orderkey = l1.l_orderkey and l3.l_suppkey <> l1.l_suppkey and l3.l_receiptdate > l3.l_commitdate ) and s_nationkey = n_nationkey and n_name = 'PERU' group by s_name order by numwait desc, s_name limit 100; -
Q22
select cntrycode, count(*) as numcust, sum(c_acctbal) as totacctbal from ( select substring(c_phone from 1 for 2) as cntrycode, c_acctbal from customer where substring(c_phone from 1 for 2) in ('24', '32', '17', '18', '12', '14', '22') and c_acctbal > ( select avg(c_acctbal) from customer where c_acctbal > 0.00 and substring(c_phone from 1 for 2) in ('24', '32', '17', '18', '12', '14', '22') ) and not exists ( select * from orders where o_custkey = c_custkey ) ) as custsale group by cntrycode order by cntrycode;