Hot-warm-cold architecture in Elasticsearch - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the Hot-warm-cold architecture in Elasticsearch?

It is a way to organize data nodes based on how often data is accessed: Hot nodes store recent, frequently accessed data; Warm nodes store older, less accessed data; Cold nodes store rarely accessed, long-term data.

beginner

Why use Hot nodes in Hot-warm-cold architecture?

Hot nodes handle new and frequently searched data. They have fast storage and more CPU to quickly index and search recent data.

intermediate

What type of hardware is typical for Cold nodes?

Cold nodes usually have slower, cheaper storage and less CPU because they store data that is rarely searched but must be kept for long-term.

intermediate

How does data move between Hot, Warm, and Cold nodes?

Data moves from Hot to Warm to Cold as it ages and is accessed less, helping save costs while keeping data available.

beginner

What is a key benefit of using Hot-warm-cold architecture?

It balances cost and performance by using expensive fast hardware for recent data and cheaper slower hardware for old data.

Which node type in Hot-warm-cold architecture stores the most recent data?

A Cold node

B Warm node

C Hot node

D Frozen node

Hot nodes store the newest and most frequently accessed data.

What is the main characteristic of Warm nodes?

A Store older data with moderate access on balanced hardware

B Store rarely accessed data on slow disks

C Store new data with fast CPUs

D Store data only in memory

Warm nodes hold older data that is accessed less often but still needs reasonable performance.

Why are Cold nodes typically cheaper in hardware?

A They do not store any data

B They use SSDs and high CPU

C They store only indexes

D They use slower disks and less CPU

Cold nodes use slower, cheaper storage and less CPU because data is rarely accessed.

What happens to data as it ages in Hot-warm-cold architecture?

A It moves from Cold to Warm to Hot nodes

B It moves from Hot to Warm to Cold nodes

C It stays only on Hot nodes

D It is deleted immediately

Data moves from Hot to Warm to Cold nodes as it becomes older and less accessed.

What is a main goal of Hot-warm-cold architecture?

A Balance cost and performance by tiering data storage

B Avoid using Cold nodes

C Store all data in memory

D Maximize storage cost by using only fast disks

The architecture balances cost and performance by using different hardware for different data ages.

Explain the Hot-warm-cold architecture and how it helps manage Elasticsearch data.

Think about how data changes over time and how hardware can match that.

Describe the hardware differences between Hot, Warm, and Cold nodes and why they matter.

Consider what kind of data each node handles and how often it is accessed.

Practice

1. What is the main purpose of the hot-warm-cold architecture in Elasticsearch?

easy

A. To encrypt data at rest and in transit

B. To store recent data on fast nodes and older data on slower, cheaper nodes

C. To backup data to external storage automatically

D. To replicate data across multiple clusters for high availability

Solution

1. Step 1: Understand the architecture purpose

   The hot-warm-cold architecture is designed to optimize storage costs and performance by placing recent data on fast nodes and older data on slower, cheaper nodes.

2. Step 2: Match the purpose to options

   To store recent data on fast nodes and older data on slower, cheaper nodes correctly describes this purpose, while other options describe different Elasticsearch features.

3. Final Answer:

   To store recent data on fast nodes and older data on slower, cheaper nodes -> Option B

4. Quick Check:

   Hot-warm-cold architecture = store data by age and speed [OK]

Hint: Remember: hot = fast recent, cold = slow old data [OK]

Common Mistakes:

• Confusing hot-warm-cold with backup or replication
• Thinking it encrypts data automatically
• Assuming it manages cluster replication

2. Which Elasticsearch feature is used to automate moving data between hot, warm, and cold phases?

easy

A. Snapshot and Restore

B. Document Level Security

C. Index Lifecycle Management (ILM)

D. Cross-cluster Search

Solution

1. Step 1: Identify automation for data phase movement

   Index Lifecycle Management (ILM) automates moving indices through hot, warm, and cold phases based on policies.

2. Step 2: Compare other features

   Snapshot and Restore handles backups, Cross-cluster Search queries multiple clusters, and Document Level Security controls access, so they don't automate data movement.

3. Final Answer:

   Index Lifecycle Management (ILM) -> Option C

4. Quick Check:

   ILM automates data phase transitions [OK]

Hint: ILM = automates index phase changes [OK]

Common Mistakes:

• Choosing Snapshot instead of ILM
• Confusing security features with lifecycle management
• Thinking cross-cluster search manages data phases

3. Given this ILM policy snippet, what phase will the index move to after 30 days?

{
  "phases": {
    "hot": {"min_age": "0d"},
    "warm": {"min_age": "7d"},
    "cold": {"min_age": "30d"}
  }
}

medium

A. Cold phase

B. Warm phase

C. Hot phase

D. Delete phase

Solution

1. Step 1: Analyze min_age values for phases

   The policy defines hot from 0 days, warm from 7 days, and cold from 30 days.

2. Step 2: Determine phase after 30 days

   After 30 days, the index reaches the cold phase because its min_age is 30 days, which is the threshold for cold.

3. Final Answer:

   Cold phase -> Option A

4. Quick Check:

   30 days = cold phase start [OK]

Hint: Check min_age values to find current phase [OK]

Common Mistakes:

• Choosing warm phase after 30 days
• Confusing delete phase with cold phase
• Ignoring min_age thresholds

4. You wrote this ILM policy but your index never moves to the warm phase:

{
  "phases": {
    "hot": {"min_age": "0d"},
    "warm": {"min_age": "10d"}
  }
}

What is the likely problem?

medium

A. The index size is too small to trigger rollover

B. The warm phase min_age is too low

C. The warm phase is missing an allocation action

D. The policy lacks a cold phase

Solution

1. Step 1: Understand ILM phase transition requirements

   For an index to move from hot to warm, rollover conditions like size or age must be met.

2. Step 2: Identify missing trigger

   If the index size is too small, rollover won't happen, so the index stays in hot phase and never moves to warm.

3. Final Answer:

   The index size is too small to trigger rollover -> Option A

4. Quick Check:

   Small index size blocks rollover and phase move [OK]

Hint: Check rollover conditions to enable phase change [OK]

Common Mistakes:

• Assuming missing allocation causes no move
• Thinking warm phase min_age is too low
• Believing cold phase is required to move to warm

5. You want to optimize storage costs by moving indices older than 60 days to cold nodes and delete indices older than 90 days. Which ILM policy snippet correctly implements this?

hard

A. { "phases": { "hot": {"min_age": "0d"}, "warm": {"min_age": "30d"}, "cold": {"min_age": "90d"}, "delete": {"min_age": "90d"} } }

B. { "phases": { "hot": {"min_age": "0d"}, "warm": {"min_age": "30d"}, "delete": {"min_age": "60d"} } }

C. { "phases": { "hot": {"min_age": "0d"}, "warm": {"min_age": "60d"}, "cold": {"min_age": "90d"}, "delete": {"min_age": "120d"} } }

D. { "phases": { "hot": {"min_age": "0d"}, "cold": {"min_age": "60d"}, "delete": {"min_age": "90d"} } }

Solution

1. Step 1: Identify required phase ages

   Indices older than 60 days should move to cold, and older than 90 days should be deleted.

2. Step 2: Match policy phases to requirements

   { "phases": { "hot": {"min_age": "0d"}, "cold": {"min_age": "60d"}, "delete": {"min_age": "90d"} } } has hot at 0d, cold at 60d, and delete at 90d, matching the requirements exactly.

3. Final Answer:

   { "phases": { "hot": {"min_age": "0d"}, "cold": {"min_age": "60d"}, "delete": {"min_age": "90d"} } } -> Option D

4. Quick Check:

   60d cold and 90d delete phases match [OK]

Hint: Match min_age exactly to your data lifecycle needs [OK]

Common Mistakes:

• Adding unnecessary warm phase with wrong min_age
• Setting delete phase too early
• Skipping cold phase before delete