Deleting and closing indexes in Elasticsearch - Deep Dive

Overview - Deleting and closing indexes

What is it?

In Elasticsearch, indexes are collections of documents that store data. Deleting an index removes it completely from the system, freeing up space but losing all its data. Closing an index temporarily disables it, making it unavailable for search or write operations but keeping its data on disk for later use.

Why it matters

Managing indexes by deleting or closing them helps keep Elasticsearch efficient and organized. Without these actions, your system could fill up with unused data, slowing down searches and wasting storage. Properly deleting or closing indexes ensures your data system stays fast and cost-effective.

Where it fits

Before learning about deleting and closing indexes, you should understand what an Elasticsearch index is and how data is stored and searched. After this, you can learn about index lifecycle management and optimizing cluster performance.

Mental Model

Core Idea

Deleting removes an index and its data permanently, while closing temporarily disables it without losing data.

Think of it like...

Think of an index like a book in a library. Deleting the index is like throwing the book away forever. Closing the index is like putting the book on a shelf in a back room where no one can read it, but it’s still there if you want it later.

┌───────────────┐       ┌───────────────┐
│   Open Index  │──────▶│  Searchable   │
│ (Active Data) │       │  & Writable   │
└───────────────┘       └───────────────┘
        │                      ▲
        │                      │
        ▼                      │
┌───────────────┐       ┌───────────────┐
│  Close Index  │──────▶│  Not Searchable│
│ (Data Stored) │       │  & Not Writable│
└───────────────┘       └───────────────┘
        │                      ▲
        │                      │
        ▼                      │
┌───────────────┐              │
│ Delete Index  │──────────────┘
│ (Data Gone)   │
└───────────────┘

Build-Up - 7 Steps

1

Foundation

What is an Elasticsearch index

Concept: An index is a collection of documents organized for fast search and retrieval.

Imagine a big folder where you keep many papers. Each paper is a document, and the folder is the index. Elasticsearch uses indexes to organize data so it can find information quickly.

Result

You understand that an index holds data and is the main way Elasticsearch organizes information.

Knowing what an index is helps you see why managing it matters for data storage and search speed.

2

Foundation

Difference between open and closed indexes

Concept: Open indexes are active and searchable; closed indexes are inactive but stored.

When an index is open, you can search and add data to it. When closed, it’s like putting the folder away; you can’t use it until you open it again, but the papers inside are still there.

Result

You can tell if an index is ready for use or temporarily disabled.

Understanding open vs closed states prepares you to manage resources without losing data.

3

Intermediate

How to close an index in Elasticsearch

🤔Before reading on: do you think closing an index deletes its data or just disables access? Commit to your answer.

Concept: Closing an index disables it without deleting its data, freeing resources but keeping data intact.

You use the Elasticsearch API command: POST /index_name/_close to close an index. This stops search and write operations but keeps the index data on disk.

Result

The index is no longer searchable or writable but can be reopened later.

Knowing how to close indexes helps manage system resources without losing data.

4

Intermediate

How to delete an index in Elasticsearch

🤔Before reading on: do you think deleting an index can be undone? Commit to your answer.

Concept: Deleting an index removes it and all its data permanently from Elasticsearch.

You use the Elasticsearch API command: DELETE /index_name to delete an index. This frees up storage but means the data is lost forever.

Result

The index and its data are completely removed from the system.

Understanding deletion is critical to avoid accidental data loss.

5

Intermediate

When to close vs delete indexes

🤔Before reading on: do you think closing is better for long-term storage or short-term resource saving? Commit to your answer.

Concept: Closing is for temporary inactivity; deleting is for permanent removal.

Close indexes when you want to save resources but keep data for later. Delete indexes when data is no longer needed and you want to free space.

Result

You can choose the right action based on your data lifecycle needs.

Knowing when to close or delete prevents wasted resources or accidental data loss.

6

Advanced

Impact of closing indexes on cluster resources

🤔Before reading on: do you think closed indexes still use memory and CPU? Commit to your answer.

Concept: Closed indexes release memory and CPU but keep disk space until deleted.

When an index is closed, Elasticsearch unloads it from memory and stops tracking it for search, reducing CPU and RAM use. However, the data files remain on disk, so storage is still used.

Result

Cluster performance improves with fewer open indexes, but disk usage remains until deletion.

Understanding resource impact helps optimize cluster performance and cost.

7

Expert

Risks and recovery with deleting and closing indexes

🤔Before reading on: can you recover a deleted index without backup? Commit to your answer.

Concept: Deleted indexes cannot be recovered without backups; closed indexes can be reopened anytime.

Deleting an index permanently removes data. Without snapshots or backups, recovery is impossible. Closed indexes can be reopened with POST /index_name/_open, restoring full access instantly.

Result

You know the importance of backups before deletion and the safety of closing indexes.

Recognizing recovery limits prevents costly data loss and informs backup strategies.

Under the Hood

Elasticsearch stores index data as files on disk and loads metadata and data structures into memory when open. Closing an index unloads these from memory, stopping search and write operations, but leaves the files intact. Deleting an index removes all files and metadata, freeing disk space and removing the index from the cluster state.

Why designed this way?

This design balances performance and resource use. Keeping indexes open allows fast search but uses memory and CPU. Closing indexes frees these resources without losing data, useful for infrequently accessed data. Deletion frees disk space permanently. Alternatives like always deleting would risk data loss; never closing would waste resources.

┌───────────────┐
│  Disk Storage │<──────────────┐
└───────────────┘               │
        ▲                      │
        │                      │
┌───────────────┐       ┌───────────────┐
│  Open Index   │──────▶│  Memory & CPU │
│ (Loaded Data) │       │  Resources    │
└───────────────┘       └───────────────┘
        │                      ▲
        │                      │
        ▼                      │
┌───────────────┐       ┌───────────────┐
│ Closed Index  │       │  No Memory or │
│ (Data on Disk)│       │  CPU Usage    │
└───────────────┘       └───────────────┘
        │                      ▲
        │                      │
        ▼                      │
┌───────────────┐              │
│Deleted Index  │──────────────┘
│ (Files Removed)│
└───────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Does closing an index delete its data? Commit to yes or no.

Common Belief: Closing an index deletes its data to save space.

Reality: Closing only disables access but keeps all data on disk intact.

Why it matters: Believing closing deletes data can cause unnecessary data loss if users delete instead of close.

Quick: Can you recover a deleted index without backup? Commit to yes or no.

Common Belief: Deleted indexes can be recovered easily from Elasticsearch automatically.

Reality: Once deleted, indexes cannot be recovered without prior snapshots or backups.

Why it matters: Assuming easy recovery leads to careless deletion and permanent data loss.

Quick: Do closed indexes consume the same memory and CPU as open ones? Commit to yes or no.

Common Belief: Closed indexes use the same cluster resources as open indexes.

Reality: Closed indexes free memory and CPU but still use disk space.

Why it matters: Misunderstanding resource use can cause inefficient cluster management and unexpected costs.

Quick: Does deleting an index immediately improve search speed? Commit to yes or no.

Common Belief: Deleting any index instantly speeds up all searches.

Reality: Deleting irrelevant indexes helps, but deleting needed indexes breaks searches and causes errors.

Why it matters: Deleting important indexes harms system functionality and user experience.

Expert Zone

1

Closed indexes do not participate in cluster state updates, reducing cluster coordination overhead.

2

Deleting large indexes can cause cluster performance spikes due to file system cleanup and metadata updates.

3

Reopening a closed index requires reloading mappings and data structures, which can temporarily impact cluster responsiveness.

When NOT to use

Avoid closing indexes if you need constant real-time access; use index lifecycle management to automate deletion for obsolete data. Avoid deleting indexes without backups or snapshots; instead, use snapshot and restore for data safety.

Production Patterns

In production, teams close old monthly indexes to save memory but keep data for audits. They delete indexes after backups and retention periods expire. Automated scripts and index lifecycle policies manage these actions to balance performance and storage costs.

Connections

Database Backup and Restore

Builds-on

Understanding deleting indexes highlights the importance of backups to recover lost data, connecting Elasticsearch management to general database safety practices.

Operating System File Management

Same pattern

Closing an index is like unmounting a disk partition—data remains on disk but is inactive—while deleting is like formatting, removing all data. This helps understand resource management at the system level.

Project Management Archiving

Opposite

Closing indexes is like archiving project files for future use, while deleting is like shredding documents. This cross-domain link clarifies temporary vs permanent data handling.

Common Pitfalls

#1 Accidentally deleting an index instead of closing it.

Wrong approach:

DELETE /my_index

Correct approach:

POST /my_index/_close

Root cause:

Confusing the API commands and not understanding the difference between deleting and closing.

#2 Trying to search a closed index and getting errors.

Wrong approach:

GET /my_closed_index/_search {"query": {"match_all": {}}}

Correct approach:

POST /my_closed_index/_open
GET /my_closed_index/_search {"query": {"match_all": {}}}

Root cause:

Not realizing closed indexes are inactive and must be reopened before use.

#3 Assuming closed indexes free disk space.

Wrong approach:

Close index and expect disk usage to drop immediately.

Correct approach:

Delete index to free disk space after closing if data is no longer needed.

Root cause:

Misunderstanding resource usage differences between closing and deleting.

Key Takeaways

Deleting an Elasticsearch index permanently removes its data and cannot be undone without backups.

Closing an index temporarily disables it, freeing memory and CPU but keeping data on disk for later use.

Use closing to save resources when data is not needed immediately but must be preserved.

Always backup important data before deleting indexes to avoid irreversible loss.

Understanding the difference between deleting and closing helps manage Elasticsearch clusters efficiently and safely.