How to Build Reliable Search Inspired by ClickUp

ClickUp shows how powerful, reliable search can transform how teams work, especially when growing past the limits of a single search engine like Elasticsearch. This step-by-step guide walks you through how to design and implement a robust search system using the same principles highlighted in the original ClickUp engineering story about Elasticsearch alternatives.

Step 1: Understand Why ClickUp Outgrew a Single Search Engine

Before building or improving your search, you need to understand the typical problems that appear at scale. In the source article, the team behind ClickUp explains that relying only on Elasticsearch eventually limited their performance and flexibility.

Key challenges they faced include:

• Search latency increasing as data volumes grew
• Difficulty modeling complex relationships between documents
• Operational overhead of scaling a single search cluster
• Need for more reliable backups and observability

Use these challenges as a checklist to evaluate your current search approach. If you see similar issues, it is time to refactor your architecture.

Step 2: Map Your Data Like ClickUp Mapped Workspace Content

The ClickUp platform handles tasks, docs, comments, custom fields, and many interconnected entities. To power accurate search, the team first needed a clear map of what data existed and how it related.

Follow a similar process:

1. List your entities
   Examples: projects, tasks, documents, comments, users, tags.

2. Define relationships
   For example: a task belongs to a project, has many comments, and is assigned to one or more users.

3. Identify searchable fields
   Titles, descriptions, user names, tags, and custom fields that should appear in results.

4. Mark non-searchable data
   Internal IDs, logs, or system metadata that should not be indexed.

This modeling step mirrors how ClickUp evaluated their workspace objects before exploring Elasticsearch alternatives.

Step 3: Separate Storage From Search Like ClickUp

An important lesson from the ClickUp article is to clearly separate your primary data store from your search layer. Elasticsearch and similar engines are excellent for search, but should not be your single source of truth.

To follow this pattern:

• Use a relational or document database as your primary data store
• Treat the search index as a projection of that data
• Design processes to rebuild or repair the index if needed

This separation improves reliability and makes it easier to evolve your search technology without rewriting your entire application, just as ClickUp did when they reconsidered their search stack.

Step 4: Design Your Indexing Pipeline Using ClickUp Principles

The ClickUp engineering team emphasizes moving toward architectures that are resilient and observable. Your indexing pipeline should reflect that mindset.

Plan an Event-Driven Indexing Flow

Instead of writing to search directly on every request, follow an event-driven process:

1. Capture changes
   Whenever data is created, updated, or deleted, emit a structured event.

2. Use a queue or stream
   Push events into a message broker so your application and search system are decoupled.

3. Index asynchronously
   Workers consume events and update the search index in the background.

4. Track failures
   Implement retry logic and dead-letter queues for problematic events.

This strategy allows your application to stay responsive even when search is under load, similar to the scalable patterns ClickUp moved toward.

Model Indexes Around Real User Queries

The original story about ClickUp and Elasticsearch alternatives highlights that search must reflect how users actually query the data. Design your indexes around user needs, not just database schemas.

Ask:

• What are the top 10 searches your users run daily?
• Which filters and sorting options matter most?
• Do users search across all content or mainly by space, folder, or project?

Then structure your index mappings and fields so those key searches are fast and relevant.

Step 5: Improve Relevance as Carefully as ClickUp

As noted in the ClickUp article, simply indexing data is not enough. You must tune relevance to match user expectations, especially in complex workspaces.

Define a Ranking Strategy

Create a ranking model based on signals, for example:

• Text match score (title, description, comments)
• Recency of updates
• User ownership or assignment
• Task or document status
• Workspace or folder priority

Combine these scores so that highly relevant, recently used items rise to the top. This mirrors how ClickUp prioritizes results that matter most to active teams.

Use Facets and Filters to Mirror ClickUp Workflows

In a complex productivity platform like ClickUp, users filter by status, assignee, tags, or locations. Your search interface should provide similar options aligned with your domain.

Design filters such as:

• Status (open, in progress, closed)
• Owner or assignee
• Date ranges
• Labels or categories
• Project or folder

Implementing filters directly in the search engine query layer keeps performance high while preserving user flexibility.

Step 6: Plan for Scale and Reliability Like ClickUp

The core of the original ClickUp article is about scaling beyond a single Elasticsearch deployment. To follow this approach, design for growth from the beginning.

Introduce Observability and Metrics

Track key metrics, including:

• Search latency (p50, p95, p99)
• Indexing lag and queue size
• Error rates and timeouts
• Cluster resource usage (CPU, memory, storage)

Dashboards and alerts help you react before users feel performance problems.

Use Backups and Recovery Plans

Like the team behind ClickUp, assume that individual nodes or even entire clusters can fail. Prepare by:

• Scheduling regular backups of primary data and index metadata
• Automating index rebuild procedures
• Documenting recovery runbooks for your operations team

This makes your search infrastructure resilient rather than fragile.

Step 7: Continuously Iterate Like ClickUp’s Team

The story of how ClickUp evaluated Elasticsearch alternatives underlines that search systems are never truly “finished.” User behavior, data volume, and performance requirements all change over time.

Build a continuous improvement loop:

1. Collect feedback
   Ask users which results are missing or irrelevant.

2. Monitor query patterns
   Log search terms and analyze trends.

3. Adjust ranking rules
   Refine weights and filters to better match behavior.

4. Run A/B tests
   Compare new relevance strategies against the current baseline.

This iterative mindset is one of the key reasons ClickUp can support complex workspaces without sacrificing usability.

Next Steps and Additional Resources

By following the architectural principles shared in the ClickUp engineering story, you can design a search experience that scales with your users, keeps performance steady, and remains maintainable for your team.

To go further:

• Study the full technical background in the official ClickUp article on Elasticsearch alternatives.
• Explore implementation strategies, architecture reviews, and optimization consulting from specialists at Consultevo.

Use these lessons from ClickUp as a blueprint to build search that stays fast, reliable, and helpful as your product grows.

Need Help With ClickUp?

If you want expert help building, automating, or scaling your ClickUp workspace, work with ConsultEvo — trusted ClickUp Solution Partners.

Get Help

“`