Advanced PostgreSQL: Indexing, Partitioning, Replication · Pelajaran

Partisi Hash untuk Distribusi

Terapkan partisi hash untuk mendistribusikan data secara merata di seluruh partisi, yang berguna untuk menghindari titik panas.

Partisi Hash untuk Distribusi adalah pelajaran Advanced PostgreSQL: Indexing, Partitioning, Replication gratis di CoddyKit. Ini adalah pelajaran 1 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar Advanced PostgreSQL: Indexing, Partitioning, Replication, dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus Advanced PostgreSQL: Indexing, Partitioning, Replication mencakup 4 pelajaran total.

Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.

What is Hash Partitioning?

Welcome to Hash Partitioning! This strategy helps distribute data evenly across multiple partitions. Instead of dividing data by ranges or specific values, it uses a hash function.

This approach is especially useful when you want to avoid 'hot spots' where one partition receives significantly more data or queries than others.

Why Use Hash Partitioning?

Hash partitioning is ideal for:

Even Distribution: It spreads data uniformly across partitions, preventing any single partition from becoming too large or slow.
Avoiding Hot Spots: Useful when your partition key doesn't have a natural order or predictable distribution (unlike time-series data for Range Partitioning).
Improved Concurrency: Queries can often operate on different partitions in parallel, boosting performance.

Hash Partitioning Syntax

To use hash partitioning, you define a parent table with PARTITION BY HASH (column_name). Then, you create child partitions using FOR VALUES WITH (MODULUS n, REMAINDER r).

MODULUS (n): The total number of partitions you plan to have.
REMAINDER (r): A unique number from 0 to n-1, indicating which 'bucket' of hashed values this partition will hold.

Creating a Hash-Partitioned Table

Let's create a table for user activity logs, partitioned by a user_id. We'll start with a parent table and two partitions. Note how the REMAINDER values cover the MODULUS.

CREATE TABLE user_activity (
  activity_id SERIAL,
  user_id INT NOT NULL,
  activity_type VARCHAR(50),
  activity_time TIMESTAMP DEFAULT NOW()
) PARTITION BY HASH (user_id);

CREATE TABLE user_activity_0
PARTITION OF user_activity
FOR VALUES WITH (MODULUS 2, REMAINDER 0);

CREATE TABLE user_activity_1
PARTITION OF user_activity
FOR VALUES WITH (MODULUS 2, REMAINDER 1);

How Data is Distributed

When you insert data, PostgreSQL calculates a hash value for the user_id. It then performs a modulo operation (hash_value % MODULUS). The result determines which partition the row belongs to.

For example, if MODULUS is 2:

If hash_value % 2 = 0, it goes to user_activity_0.
If hash_value % 2 = 1, it goes to user_activity_1.

Inserting Data Example

Let's insert some data into our user_activity table. PostgreSQL automatically directs each row to the correct hash partition based on the user_id.

Try inserting different user_id values and observe the distribution!

INSERT INTO user_activity (user_id, activity_type) VALUES
(101, 'login'),
(202, 'logout'),
(303, 'view_item'),
(404, 'add_to_cart'),
(101, 'browse_category'),
(505, 'purchase');

SELECT tableoid::regclass as partition_name, *
FROM user_activity
ORDER BY user_id;

Querying Hash Partitions

Querying a hash-partitioned table is just like querying a regular table. PostgreSQL's query planner is smart enough to use partition pruning.

If your query includes the partition key (e.g., WHERE user_id = 101), the planner will only scan the relevant partition, significantly speeding up queries.

SELECT * FROM user_activity WHERE user_id = 101;

-- To see pruning in action:
EXPLAIN SELECT * FROM user_activity WHERE user_id = 101;

Hash vs. Other Partition Types

Unlike Range partitioning (based on intervals) or List partitioning (based on specific values), Hash partitioning doesn't group data logically.

Its strength is in spreading data evenly, making it great for keys without natural ranges (like UUIDs or arbitrary IDs) and for preventing specific parts of your data from becoming bottlenecks.

Considerations & Maintenance

When using hash partitioning:

Choose your key wisely: The partition key should have a good distribution of hash values.
Modulus & Remainder: Ensure all REMAINDER values from 0 to MODULUS - 1 are covered by partitions.
Adding/Removing Partitions: Changing the MODULUS later requires re-hashing all data, which can be complex. Plan your partition count carefully.

Hash Partitioning Quiz

Which scenario is best suited for PostgreSQL hash partitioning?

Hash Partitioning Recap

In this lesson, you've learned about PostgreSQL hash partitioning:

It distributes data evenly based on a hash function of the partition key.
It uses MODULUS and REMAINDER to define partitions.
It's excellent for avoiding hot spots and improving performance for non-sequential or arbitrary keys.
Queries benefit from partition pruning when the partition key is used.

Hash partitioning is a powerful tool for scaling your database when even data spread is crucial.

Pertanyaan yang Sering Diajukan

Apakah pelajaran “Partisi Hash untuk Distribusi” gratis?

Ya — teks lengkap “Partisi Hash untuk Distribusi” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus Advanced PostgreSQL: Indexing, Partitioning, Replication, upgrade ke CoddyKit PRO. Kursus Advanced PostgreSQL: Indexing, Partitioning, Replication mencakup 4 pelajaran total.

Apa yang akan aku pelajari di “Partisi Hash untuk Distribusi”?

Terapkan partisi hash untuk mendistribusikan data secara merata di seluruh partisi, yang berguna untuk menghindari titik panas. Kamu berlatih Advanced PostgreSQL: Indexing, Partitioning, Replication dengan kode praktik yang langsung kamu jalankan di browser, dan tutor AI 24/7 menjawab pertanyaanmu saat kamu mengerjakan pelajaran ini.

Apakah aku perlu pengalaman untuk memulai Advanced PostgreSQL: Indexing, Partitioning, Replication?

Tidak diperlukan pengalaman sebelumnya. Advanced PostgreSQL: Indexing, Partitioning, Replication di CoddyKit dirancang untuk pemula hingga pelajar tingkat lanjut, jadi kamu bisa memulai di sini atau dari awal dan belajar sesuai kecepatan kamu sendiri. Ini adalah pelajaran 1 dari 4.

Berapa lama pelajaran “Partisi Hash untuk Distribusi” memakan waktu?

Sebagian besar pelajaran CoddyKit memakan waktu sekitar 5–10 menit. Setiap pelajaran ringkas dan interaktif, jadi kamu membuat kemajuan stabil dan melanjutkan dari tempat kamu tinggalkan di web dan aplikasi.

Bisakah aku menulis dan menjalankan kode dalam pelajaran Advanced PostgreSQL: Indexing, Partitioning, Replication ini?

Ya. Setiap pelajaran Advanced PostgreSQL: Indexing, Partitioning, Replication menyertakan editor kode bawaan, jadi kamu menulis dan menjalankan kode nyata langsung di browser dan mendapatkan umpan balik AI instan — tidak diperlukan penyiapan lokal.

Semua pelajaran dalam kursus ini

← Kembali ke Advanced PostgreSQL: Indexing, Partitioning, Replication