Work_mem and effective_cache_size tuning in PostgreSQL - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the work_mem setting control in PostgreSQL?

work_mem controls the amount of memory used for internal sort operations and hash tables before writing to disk. Increasing it can speed up queries that need sorting or hashing.

beginner

What is the purpose of effective_cache_size in PostgreSQL?

effective_cache_size estimates how much memory is available for disk caching by the operating system and PostgreSQL. It helps the query planner decide if using an index is likely to be fast.

intermediate

How does increasing work_mem affect query performance?

Increasing work_mem allows larger operations to happen in memory, reducing slow disk writes. But setting it too high can cause memory exhaustion if many queries run simultaneously.

intermediate

Why should effective_cache_size be set to a value close to available OS cache?

Because it tells PostgreSQL how much data it can expect to find in the OS cache. A realistic value helps the planner choose efficient query plans, like using indexes when data is likely cached.

advanced

What is a safe approach to tuning work_mem and effective_cache_size?

Start with conservative values, monitor query performance and memory usage, then gradually adjust. For work_mem, consider workload concurrency. For effective_cache_size, estimate OS cache size realistically.

What does increasing work_mem primarily improve?

A Disk space for storing tables

B Sorting and hashing operations in queries

C Network bandwidth for connections

D Number of concurrent connections

work_mem controls memory for sorting and hashing during query execution, so increasing it improves these operations.

What does effective_cache_size represent?

A Estimated memory available for disk caching by OS and PostgreSQL

B Size of the largest table in the database

C Maximum memory PostgreSQL can use for connections

D Memory available for PostgreSQL's internal cache only

effective_cache_size estimates how much memory the OS and PostgreSQL use for caching disk data, helping the query planner.

What risk comes from setting work_mem too high?

A Indexes will be ignored

B Disk space will run out

C Queries will run slower

D Memory exhaustion if many queries run at once

High work_mem per query multiplied by many concurrent queries can exhaust server memory.

Why is it important to set effective_cache_size realistically?

A To avoid PostgreSQL crashing

B To limit the number of connections

C To help the query planner choose efficient plans

D To increase disk space

A realistic effective_cache_size helps the planner estimate if indexes will be fast, improving query plans.

Which of these is a good first step when tuning work_mem and effective_cache_size?

A Start with conservative values and monitor performance

B Disable caching completely

C Set both to maximum values immediately

D Ignore <code>effective_cache_size</code> and only tune <code>work_mem</code>

Starting conservatively and monitoring helps avoid memory issues and find optimal settings.

Explain what work_mem and effective_cache_size do in PostgreSQL and why tuning them matters.

Think about memory use during query execution and how PostgreSQL plans queries.

Describe a safe approach to adjusting work_mem and effective_cache_size for better database performance.

Focus on careful testing and gradual tuning.

Practice

1. What does the work_mem setting control in PostgreSQL?

easy

A. The amount of memory used for sorting and joining operations during query execution

B. The total memory available for caching disk pages

C. The maximum size of a database connection pool

D. The memory allocated for background worker processes

Solution

1. Step 1: Understand the role of work_mem

   work_mem is the memory PostgreSQL uses for internal operations like sorting and joining during query execution.

2. Step 2: Differentiate from other memory settings

   Other settings like effective_cache_size relate to cache estimation, not sorting or joining memory.

3. Final Answer:

   The amount of memory used for sorting and joining operations during query execution -> Option A

4. Quick Check:

   work_mem = sorting/join memory [OK]

Hint: Remember: work_mem is per operation memory for sorting/joining [OK]

Common Mistakes:

• Confusing work_mem with effective_cache_size
• Thinking work_mem controls total server memory
• Assuming work_mem affects connection limits

2. Which of the following is the correct way to set effective_cache_size to 4GB in PostgreSQL's configuration file?

easy

A. effective_cache_size = 4000MB

B. effective_cache_size = '4GB'

C. effective_cache_size = 4g

D. effective_cache_size = 4GB

Solution

1. Step 1: Check PostgreSQL config syntax for memory sizes

   PostgreSQL accepts memory sizes with units like KB, MB, GB without quotes.

2. Step 2: Validate each option

   effective_cache_size = 4GB uses correct syntax: number + unit without quotes. effective_cache_size = '4GB' uses quotes (invalid). effective_cache_size = 4000MB uses MB but 4000MB is less than 4GB. effective_cache_size = 4g uses lowercase 'g' which is invalid; units must be uppercase.

3. Final Answer:

   effective_cache_size = 4GB -> Option D

4. Quick Check:

   Config memory size = number + uppercase unit [OK]

Hint: Use number + uppercase unit without quotes for memory sizes [OK]

Common Mistakes:

• Adding quotes around memory size values
• Using lowercase units like 'g' instead of 'GB'
• Confusing MB and GB values

3. Given work_mem = '2MB' and a query performing 3 sorts simultaneously, what is the total memory PostgreSQL may use for sorting?

medium

A. 6MB

B. 3MB

C. 2MB

D. 1MB

Solution

1. Step 1: Understand work_mem usage per operation

   Each sort operation can use up to work_mem memory independently.

2. Step 2: Calculate total memory for 3 sorts

   3 sorts x 2MB each = 6MB total memory used for sorting.

3. Final Answer:

   6MB -> Option A

4. Quick Check:

   work_mem x number of sorts = total memory [OK]

Hint: Multiply work_mem by number of simultaneous operations [OK]

Common Mistakes:

• Assuming work_mem is total for all operations
• Adding instead of multiplying memory sizes
• Ignoring simultaneous operation count

4. A PostgreSQL server has effective_cache_size set too low. What problem might this cause?

medium

A. PostgreSQL will allocate too much memory for sorting operations

B. The server will crash due to memory exhaustion

C. PostgreSQL may underestimate available cache and choose inefficient query plans

D. Connections will be refused due to low cache size

Solution

1. Step 1: Understand effective_cache_size role

   This setting helps PostgreSQL estimate how much OS cache is available for data pages.

2. Step 2: Consequence of low effective_cache_size

   If set too low, PostgreSQL thinks less cache is available, so it may avoid index scans or other efficient plans, choosing slower ones.

3. Final Answer:

   PostgreSQL may underestimate available cache and choose inefficient query plans -> Option C

4. Quick Check:

   Low effective_cache_size = conservative query plans [OK]

Hint: Low effective_cache_size causes conservative, slower plans [OK]

Common Mistakes:

• Confusing effective_cache_size with work_mem
• Assuming server crashes from low effective_cache_size
• Thinking it limits connection count

5. You have a PostgreSQL server with 32GB RAM. You want to optimize work_mem and effective_cache_size for a workload with many concurrent queries doing large sorts. Which is the best tuning approach?

hard

A. Set work_mem very high (e.g., 1GB) and effective_cache_size low (e.g., 4GB)

B. Set work_mem moderately (e.g., 16MB) and effective_cache_size high (e.g., 24GB)

C. Set both work_mem and effective_cache_size very low to save memory

D. Set work_mem low (e.g., 1MB) and effective_cache_size moderate (e.g., 8GB)

Solution

1. Step 1: Balance work_mem for concurrent large sorts

   Setting work_mem too high risks memory exhaustion with many queries; moderate value like 16MB balances performance and memory use.

2. Step 2: Set effective_cache_size to reflect OS cache

   With 32GB RAM, setting effective_cache_size high (e.g., 24GB) helps PostgreSQL plan queries assuming ample cache.

3. Step 3: Evaluate other options

   Set work_mem very high (e.g., 1GB) and effective_cache_size low (e.g., 4GB) risks memory overuse; Set both work_mem and effective_cache_size very low to save memory wastes performance; Set work_mem low (e.g., 1MB) and effective_cache_size moderate (e.g., 8GB) underestimates cache and limits sort memory.

4. Final Answer:

   Set work_mem moderately (e.g., 16MB) and effective_cache_size high (e.g., 24GB) -> Option B

5. Quick Check:

   Moderate work_mem + high effective_cache_size = balanced tuning [OK]

Hint: Balance work_mem and effective_cache_size for memory and cache [OK]

Common Mistakes:

• Setting work_mem too high causing memory exhaustion
• Setting effective_cache_size too low causing bad plans
• Ignoring concurrent query memory needs