Memory usage analysis (INFO memory) in Redis - Step-by-Step Execution

Concept Flow - Memory usage analysis (INFO memory)

Run INFO memory command

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Receive memory stats

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Parse key metrics

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Analyze memory usage

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Identify bottlenecks or inefficiencies

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Optimize or monitor accordingly

The flow shows running the INFO memory command, getting memory stats, parsing key metrics, analyzing usage, and then optimizing or monitoring.

Execution Sample

Redis

127.0.0.1:6379> INFO memory
# Memory
used_memory:1024000
used_memory_rss:2048000
used_memory_peak:3072000
mem_fragmentation_ratio:2.0
mem_allocator:jemalloc-5.1.0

This command shows Redis memory usage details like used memory, peak memory, and fragmentation ratio.

Execution Table

Step	Action	Metric Extracted	Value	Meaning
1	Run INFO memory command	used_memory	1024000	Bytes of memory Redis is using
2	Parse output	used_memory_rss	2048000	Memory allocated by OS (resident set size)
3	Parse output	used_memory_peak	3072000	Highest memory used since start
4	Parse output	mem_fragmentation_ratio	2.0	Memory overhead due to fragmentation
5	Parse output	mem_allocator	jemalloc-5.1.0	Memory allocator used by Redis
6	Analyze fragmentation	mem_fragmentation_ratio	2.0	High fragmentation means inefficient memory use
7	Decision	Optimize or monitor	-	If fragmentation >1.5, consider optimization
8	Exit	-	-	Analysis complete

💡 All key memory metrics extracted and analyzed for optimization decisions

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	Final
used_memory	0	1024000	1024000	1024000	1024000	1024000
used_memory_rss	0	0	2048000	2048000	2048000	2048000
used_memory_peak	0	0	0	3072000	3072000	3072000
mem_fragmentation_ratio	0	0	0	0	2.0	2.0
mem_allocator	unknown	unknown	unknown	unknown	jemalloc-5.1.0	jemalloc-5.1.0

Key Moments - 3 Insights

Why is used_memory_rss larger than used_memory?

used_memory_rss includes all memory allocated by the OS, including fragmentation and overhead, while used_memory is the actual memory Redis is using for data. See execution_table rows 1 and 2.

What does a mem_fragmentation_ratio of 2.0 mean?

It means Redis is using twice as much memory as the data size due to fragmentation or allocator overhead. High fragmentation can cause inefficiency. See execution_table row 4 and 6.

Why do we check used_memory_peak?

used_memory_peak shows the highest memory Redis has used since start, helping to understand memory spikes. See execution_table row 3.

Visual Quiz - 3 Questions

Look at the execution_table at step 4, what is the mem_fragmentation_ratio value?

A 1.0

B 2.0

C 0.5

D 3.0

💡 Hint

Check the 'Metric Extracted' and 'Value' columns at step 4 in execution_table.

At which step do we identify that memory fragmentation might be a problem?

A Step 6

B Step 5

C Step 2

D Step 7

💡 Hint

Look for the step where mem_fragmentation_ratio is analyzed in execution_table.

If used_memory_peak was lower than used_memory, what would that indicate?

A Memory usage is currently at its highest

B Memory fragmentation is low

C Memory usage has never been that high before

D Redis is using a different allocator

💡 Hint

Compare used_memory and used_memory_peak values in variable_tracker.

Concept Snapshot

Run the Redis command INFO memory to get memory stats.
Key metrics: used_memory, used_memory_rss, used_memory_peak, mem_fragmentation_ratio.
used_memory = actual data memory used.
used_memory_rss = OS memory allocated (includes fragmentation).
mem_fragmentation_ratio >1 means memory overhead.
Use these to monitor and optimize Redis memory usage.

Full Transcript

This visual execution shows how to analyze Redis memory usage using the INFO memory command. First, the command is run to get memory statistics. Then key metrics like used_memory, used_memory_rss, used_memory_peak, and mem_fragmentation_ratio are extracted. The used_memory shows actual memory used by Redis data. The used_memory_rss shows memory allocated by the operating system, which is usually larger due to fragmentation and overhead. The used_memory_peak shows the highest memory used since Redis started, helping identify memory spikes. The mem_fragmentation_ratio indicates how much extra memory is used due to fragmentation; a value of 2.0 means twice the memory is used compared to actual data size, which is inefficient. By analyzing these values, one can decide if optimization or monitoring is needed. This step-by-step trace helps beginners understand how Redis memory metrics relate and how to interpret them for better memory management.