Getting Started with TCE Search: Quick Setup and First Queries

Getting Started with TCE Search: Quick Setup and First QueriesTCE Search is a powerful search and indexing tool designed to help teams find information quickly across large datasets, codebases, and knowledge repositories. This guide walks you through setting up TCE Search, configuring its core components, and executing your first queries. It’s written for technical users and managers who want a practical, step-by-step introduction.

What is TCE Search?

TCE Search is a search platform that indexes documents, code, logs, and structured data, enabling fast, relevant retrieval. It typically provides features such as full-text search, faceted filtering, relevance tuning, and integration with data pipelines and authentication systems. Use cases include knowledge base search, enterprise document discovery, code search, and investigative analytics.

Prerequisites

Before starting, ensure you have the following:

A machine or server with at least 4 CPU cores and 8 GB RAM for small deployments (increase resources for larger datasets).
A supported OS (Linux distributions are most common for production).
Access to the data sources you want to index (file shares, databases, cloud storage, code repositories).
Basic familiarity with the command line, JSON/YAML configuration files, and network concepts.
(Optional) Docker if you prefer containerized deployment.

Installation Options

You can run TCE Search in several ways:

Local binary installation — ideal for testing and small instances.
Docker container — convenient for development and reproducible environments.
Kubernetes — recommended for scalable production deployments.
Managed/service offering — if available, this removes infrastructure management.

Below are quick instructions for the two most common approaches: Docker and local binary.

Docker (quick start)

Ensure Docker is installed and running.
Pull the TCE Search image:
```
docker pull tce/tce-search:latest 
```

Run a single-node container:


docker run -d --name tce-search -p 9200:9200 -p 9300:9300  -v tce_data:/var/lib/tce-search  tce/tce-search:latest

Check logs:
```
docker logs -f tce-search 
```
Visit the web UI at http://localhost:9200 (or the port TCE Search exposes).

Local binary

Download the latest release for your OS from your distribution point.
Unpack and move the binary to /usr/local/bin/ (or a preferred path).
Create a configuration file (example below).

Start the service:


tce-search --config /etc/tce-search/config.yml

Initial Configuration

TCE Search uses a configuration file (YAML/JSON). Key sections include:

network: host, ports, TLS settings
storage: data directories, snapshot locations
index_defaults: analyzers, tokenizers, mappings
connectors: sources to crawl/index (S3, SMB, Git, databases)
security: auth providers, API keys, roles

Example minimal config (YAML):

network:   host: 0.0.0.0   http_port: 9200 storage:   data_path: /var/lib/tce-search/data   logs_path: /var/log/tce-search index_defaults:   analyzer: standard   shards: 1   replicas: 0 connectors:   - type: filesystem     id: corpus_files     path: /data/corpus security:   api_keys_enabled: true

After configuring, restart or launch the service and confirm the node is healthy via the health API (e.g., GET /_cluster/health).

Ingesting Data

TCE Search supports multiple ingestion methods:

Connectors (recommended) — built-in crawlers for common sources.
Bulk API — upload batches of documents in JSON/NDJSON.
SDKs and client libraries — programmatic indexing from applications.
Real-time pipelines — integrate with message queues (Kafka, RabbitMQ).

Example: Filesystem connector

Configure a filesystem connector in the connectors section (see config above). Place documents into the specified path; the connector will crawl and index file metadata and content.

Example: Bulk API (NDJSON)

Prepare an NDJSON file where each pair of lines represent an action and a document:

{ "index": { "_index": "kb", "_id": "1" } } { "title": "Intro to TCE Search", "body": "TCE Search is..." } { "index": { "_index": "kb", "_id": "2" } } { "title": "Config tips", "body": "Use analyzers to..." }

Load it via curl:

curl -XPOST 'http://localhost:9200/_bulk'    -H 'Content-Type: application/x-ndjson'    --data-binary @bulk.ndjson

Mapping and Analyzers

Define index mappings to control how fields are stored and searched. Common goals:

Full-text searchable fields (use text with analyzers).
Exact-match fields (use keyword).
Date, numeric, geo types for specialized queries.

Example mapping snippet:

{   "mappings": {     "properties": {       "title": { "type": "text", "analyzer": "english" },       "tags": { "type": "keyword" },       "published": { "type": "date" },       "views": { "type": "integer" }     }   } }

Analyzers process text (tokenize, lowercase, remove stopwords, stem). Use language analyzers for better relevance.

Running Your First Queries

Most TCE Search deployments expose a RESTful search API. Basic query types:

Match query — full-text search.
Term query — exact match on keyword fields.
Range query — numeric/date ranges.
Bool query — combine clauses (must, should, must_not).
Aggregations — faceted counts, stats, histograms.

Simple match query

curl -XGET 'http://localhost:9200/kb/_search' -H 'Content-Type: application/json' -d' {   "query": {     "match": {       "body": "configuration tips"     }   } } '

curl -XGET 'http://localhost:9200/kb/_search' -H 'Content-Type: application/json' -d' {   "query": { "match_all": {} },   "aggs": {     "by_tag": {       "terms": { "field": "tags" }     }   } } '

Useful parameters

size — number of hits to return.
from — pagination offset.
sort — order results (relevance, date, custom score).
highlight — return matched snippets.

Relevance Tuning

Improve result quality by:

Choosing appropriate analyzers.
Using multi-field mappings (text + keyword).
Boosting fields: title^3, tags^2.
Implementing function_score for recency/popularity.
Adding synonyms, stopword lists, and query-time boosting.

Example multi-field query with boosts:

{   "query": {     "multi_match": {       "query": "search tips",       "fields": ["title^3", "body", "tags^2"]     }   } }

Security & Access Control

Protect your deployment:

Enable TLS for transport and HTTP APIs.
Use API keys or OAuth for application access.
Implement role-based access control (RBAC) for users and services.
Audit logs for indexing and query activity.

Monitoring & Maintenance

Key practices:

Monitor node health, CPU, memory, and disk usage.
Track index size, shard distribution, and merge activity.
Schedule snapshots/backups regularly.
Reindex when mappings change or to apply new analyzers.
Use rolling restarts for cluster upgrades.

Troubleshooting Tips

Slow queries: examine slowlog, optimize mappings, add filters.
Indexing failures: check connector logs and document schema.
Out of memory: increase heap, add nodes, or reduce shard count.
Relevance problems: tune analyzers and boosts; add synonyms.

Example Workflow: From Zero to Searchable KB

Install TCE Search via Docker.
Configure filesystem connector to /data/kb.
Place Markdown and PDF files into /data/kb.
Verify connector has indexed documents via GET /_cat/indices.
Create mappings for title, body, tags, and published fields.
Run sample match queries and add highlights.
Tune relevance using boosts and an English analyzer.
Set up TLS and API keys, then monitor performance.

Conclusion

TCE Search combines flexible ingestion, powerful query capabilities, and customization for relevance to help teams retrieve the right information fast. Start small with a single-node Docker setup, index a representative dataset, and iteratively tune mappings, analyzers, and relevance to fit your users’ needs.

If you want, tell me your environment (OS, data types, deployment preference) and I’ll give a tailored setup and sample configs.