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Elasticsearch File Ingest

  • 2.3k installs
  • 546 repo stars
  • Updated July 22, 2026
  • elastic/agent-skills

elasticsearch-file-ingest is an agent skill for >

About

> The elasticsearch-file-ingest skill documents workflows and patterns from the repository SKILL.md. --- name: elasticsearch-file-ingest description: > Ingest and transform data files (CSV/JSON/Parquet/Arrow IPC) into Elasticsearch with stream processing and custom transforms. Use when loading files or batch importing data - not for reindexing, general ingest pipeline design, or bulk API patterns. metadata: author: elastic version: 0.2.0 --- # Elasticsearch File Ingest Stream-based ingestion and transformation of large data files (NDJSON, CSV, Parquet, Arrow IPC) into Elasticsearch. ## Features & Use Cases - **Stream-based**: Handle large files without running out of memory - **High throughput**: 50k+ documents/second on commodity hardware - **Formats**: NDJSON, CSV, Parquet, Arrow IPC - **Transformations**: Apply custom JavaScript transforms during ingestion (enrich, split, filter) - **Batch processing**: Ingest multiple files matching a pattern (e.g., `logs/*.json`) - **Document splitting**: Transform one source document into multiple targets ## Prerequisites - **Elasticsearch 8.x or 9.x** accessible (local or remote) - **Node.js 22+** installed ## Setup This skill is self-con.

  • Elasticsearch File Ingest
  • **Stream-based**: Handle large files without running out of memory
  • **High throughput**: 50k+ documents/second on commodity hardware
  • **Formats**: NDJSON, CSV, Parquet, Arrow IPC
  • **Transformations**: Apply custom JavaScript transforms during ingestion (enrich, split, filter)

Elasticsearch File Ingest by the numbers

  • 2,349 all-time installs (skills.sh)
  • +170 installs in the week ending Aug 5, 2026 (Skillselion tracking)
  • Ranked #251 of 1,879 Marketing & SEO skills by installs in the Skillselion catalog
  • Security screen: HIGH risk (skills.sh audit)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
At a glance

elasticsearch-file-ingest capabilities & compatibility

Capabilities
elasticsearch file ingest · **stream based**: handle large files without run · **high throughput**: 50k+ documents/second on co · **formats**: ndjson, csv, parquet, arrow ipc · **transformations**: apply custom javascript tra
Use cases
documentation
From the docs

What elasticsearch-file-ingest says it does

--- name: elasticsearch-file-ingest description: > Ingest and transform data files (CSV/JSON/Parquet/Arrow IPC) into Elasticsearch with stream processing and custom transforms.
SKILL.md
metadata: author: elastic version: 0.2.0 --- # Elasticsearch File Ingest Stream-based ingestion and transformation of large data files (NDJSON, CSV, Parquet, Arrow IPC) into Elasticsearch.
SKILL.md
The `scripts/` folder and `package.json` live in this skill's directory.
SKILL.md
npx skills add https://github.com/elastic/agent-skills --skill elasticsearch-file-ingest

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Listed on Skillselion
Installs2.3k
repo stars546
Security audit2 / 3 scanners passed
Last updatedJuly 22, 2026
Repositoryelastic/agent-skills

What problem does elasticsearch-file-ingest solve for developers using the documented workflows?

>

Who is it for?

Developers working with elasticsearch-file-ingest patterns described in the skill documentation.

Skip if: Skip when docs are empty or the task is outside the skill documented scope.

When should I use this skill?

>

What you get

Grounded guidance and workflows from SKILL.md for elasticsearch-file-ingest.

  • Index mapping JSON
  • Transform script
  • Bulk-indexed documents

By the numbers

  • Example mapping covers 6 top-level field groups including nested user.id, user.name, and user.email

Files

SKILL.mdMarkdownGitHub ↗

Elasticsearch File Ingest

Stream-based ingestion and transformation of large data files (NDJSON, CSV, Parquet, Arrow IPC) into Elasticsearch.

Features & Use Cases

  • Stream-based: Handle large files without running out of memory
  • High throughput: 50k+ documents/second on commodity hardware
  • Formats: NDJSON, CSV, Parquet, Arrow IPC
  • Transformations: Apply custom JavaScript transforms during ingestion (enrich, split, filter)
  • Batch processing: Ingest multiple files matching a pattern (e.g., logs/*.json)
  • Document splitting: Transform one source document into multiple targets

Prerequisites

  • Elasticsearch 8.x or 9.x accessible (local or remote)
  • Node.js 22+ installed

Setup

This skill is self-contained. The scripts/ folder and package.json live in this skill's directory. Run all commands from this directory. Use absolute paths when referencing data files located elsewhere.

Before first use, install dependencies:

npm install

Environment Configuration

Elasticsearch connection is configured by users exclusively via environment variables. Never pass credentials as command-line arguments. If the test fails, output the setup options below to the user, then stop. Do not proceed with ingestion until a successful connection test.

Option 1: Elastic Cloud (recommended for production)
export ELASTICSEARCH_CLOUD_ID="<your-cloud-id>"
export ELASTICSEARCH_API_KEY="<your-api-key>"
Option 2: Direct URL with API Key
export ELASTICSEARCH_URL="https://elasticsearch:9200"
export ELASTICSEARCH_API_KEY="<your-api-key>"
Option 3: Basic Authentication
export ELASTICSEARCH_URL="https://elasticsearch:9200"
export ELASTICSEARCH_USERNAME="<your-username>"
export ELASTICSEARCH_PASSWORD="<your-password>"
Option 4: Local Development

For local development and testing, see Run Elasticsearch locally to spin up Elasticsearch and Kibana. After setup, export the connection variables (URL and API key or credentials) as shown in Option 2 or Option 3 above.

Optional: Skip TLS verification (development only)
export ELASTICSEARCH_INSECURE="true"

Test Connection

Verify the Elasticsearch connection before ingesting data:

node scripts/ingest.js test

Always run this first. If the test fails, resolve the connection issue before proceeding.

Examples

Ingest a JSON file

node scripts/ingest.js ingest --file /absolute/path/to/data.json --target my-index

Stream NDJSON/CSV via stdin

# NDJSON
cat /absolute/path/to/data.ndjson | node scripts/ingest.js ingest --stdin --target my-index

# CSV
cat /absolute/path/to/data.csv | node scripts/ingest.js ingest --stdin --source-format csv --target my-index

Ingest CSV directly

node scripts/ingest.js ingest --file /absolute/path/to/users.csv --source-format csv --target users

Ingest Parquet directly

node scripts/ingest.js ingest --file /absolute/path/to/users.parquet --source-format parquet --target users

Ingest Arrow IPC directly

node scripts/ingest.js ingest --file /absolute/path/to/users.arrow --source-format arrow --target users

Ingest CSV with parser options

# csv-options.json
# {
#   "columns": true,
#   "delimiter": ";",
#   "trim": true
# }

node scripts/ingest.js ingest --file /absolute/path/to/users.csv --source-format csv --csv-options csv-options.json --target users

Infer mappings/pipeline from CSV

When using --infer-mappings, do not combine it with --source-format csv. Inference sends a raw sample to Elasticsearch's _text_structure/find_structure endpoint, which returns both mappings and an ingest pipeline with a CSV processor. If --source-format csv is also set, CSV is parsed client-side and server-side, resulting in an empty index. Let --infer-mappings handle everything:

node scripts/ingest.js ingest --file /absolute/path/to/users.csv --infer-mappings --target users

Infer mappings with options

# infer-options.json
# {
#   "sampleBytes": 200000,
#   "lines_to_sample": 2000
# }

node scripts/ingest.js ingest --file /absolute/path/to/users.csv --infer-mappings --infer-mappings-options infer-options.json --target users

Ingest with custom mappings

node scripts/ingest.js ingest --file /absolute/path/to/data.json --target my-index --mappings mappings.json

Ingest with transformation

node scripts/ingest.js ingest --file /absolute/path/to/data.json --target my-index --transform transform.js

Command Reference

Required Options

--target <index>         # Target index name

Source Options (choose one)

--file <path>            # Source file (supports wildcards, e.g., logs/*.json)
--stdin                  # Read NDJSON/CSV from stdin

Index Configuration

--mappings <file.json>          # Mappings file
--infer-mappings                # Infer mappings/pipeline from file/stream (do NOT combine with --source-format)
--infer-mappings-options <file> # Options for inference (JSON file)
--delete-index                  # Delete target index if exists
--pipeline <name>               # Ingest pipeline name

Processing

--transform <file.js>    # Transform function (export as default or module.exports)
--source-format <fmt>    # Source format: ndjson|csv|parquet|arrow (default: ndjson)
--csv-options <file>     # CSV parser options (JSON file)
--skip-header            # Skip first line (e.g., CSV header)

Performance

--buffer-size <kb>       # Buffer size in KB (default: 5120)
--total-docs <n>         # Total docs for progress bar (file/stream)
--stall-warn-seconds <n> # Stall warning threshold (default: 30)
--progress-mode <mode>   # Progress output: auto|line|newline (default: auto)
--debug-events           # Log pause/resume/stall events
--quiet                  # Disable progress bars

Transform Functions

Transform functions let you modify documents during ingestion. Create a JavaScript file that exports a transform function:

Basic Transform (transform.js)

// ES modules (default)
export default function transform(doc) {
  return {
    ...doc,
    full_name: `${doc.first_name} ${doc.last_name}`,
    timestamp: new Date().toISOString(),
  };
}

// Or CommonJS
module.exports = function transform(doc) {
  return {
    ...doc,
    full_name: `${doc.first_name} ${doc.last_name}`,
  };
};

Skip Documents

Return null or undefined to skip a document:

export default function transform(doc) {
  // Skip invalid documents
  if (!doc.email || !doc.email.includes("@")) {
    return null;
  }
  return doc;
}

Split Documents

Return an array to create multiple target documents from one source:

export default function transform(doc) {
  // Split a tweet into multiple hashtag documents
  const hashtags = doc.text.match(/#\w+/g) || [];
  return hashtags.map((tag) => ({
    hashtag: tag,
    tweet_id: doc.id,
    created_at: doc.created_at,
  }));
}

Mappings

Custom Mappings (mappings.json)

{
  "properties": {
    "@timestamp": { "type": "date" },
    "message": { "type": "text" },
    "user": {
      "properties": {
        "name": { "type": "keyword" },
        "email": { "type": "keyword" }
      }
    }
  }
}
node scripts/ingest.js ingest --file /absolute/path/to/data.json --target my-index --mappings mappings.json

Boundaries

  • Never echo, print, log, or otherwise reveal the values of credential environment variables

($ELASTICSEARCH_API_KEY, $ELASTICSEARCH_PASSWORD, $ELASTICSEARCH_CLOUD_ID, etc.). Do not run shell commands whose output would expose secret values (e.g., echo $ELASTICSEARCH_API_KEY, env | grep KEY, printenv). Exporting these variables and running scripts that read them internally is expected and safe — the restriction is on surfacing secret values in command output. The only way to verify connectivity is node scripts/ingest.js test. If the test fails, ask the user to check their environment configuration — do not attempt to diagnose credentials yourself.

  • Never run destructive commands (such as using the --delete-index flag or deleting existing indices and data)

without explicit user confirmation.

Guidelines

  • Test first: Always run node scripts/ingest.js test before ingesting data. If the connection fails, ask the user

to verify their environment configuration and re-test. Do not attempt ingestion until the test passes.

  • Never combine `--infer-mappings` with `--source-format`. Inference creates a server-side ingest pipeline that

handles parsing (e.g., CSV processor). Using --source-format csv parses client-side as well, causing double-parsing and an empty index. Use --infer-mappings alone for automatic detection, or --source-format with explicit --mappings for manual control.

  • Use `--source-format csv` with `--mappings` when you want client-side CSV parsing with known field types.
  • Use `--infer-mappings` alone when you want Elasticsearch to detect the format, infer field types, and create an

ingest pipeline automatically.

When NOT to Use

Consider alternatives for:

  • Reindexing or index migration: Use the elasticsearch-reindex skill for copying, migrating, or transforming

existing Elasticsearch indices

Elastic Agent

  • Enterprise pipelines: Use Logstash
  • Built-in transforms: Use

Elasticsearch Transforms

Additional Resources

  • Common Patterns - Detailed examples for CSV loading, batch ingestion, enrichment, and more
  • Troubleshooting - Solutions for common issues

References

Related skills

Forks & variants (1)

Elasticsearch File Ingest has 1 known copy in the catalog totaling 2 installs. They canonicalize to this original listing.

How it compares

Choose elasticsearch-file-ingest when you need agent-guided mapping plus Node transform hooks for one-off or batch files rather than streaming Logstash pipelines.

FAQ

Who is Elasticsearch File Ingest for?

Developers and software engineers working with elasticsearch-file-ingest patterns from the skill documentation.

When should I use Elasticsearch File Ingest?

>

Is Elasticsearch File Ingest safe to install?

Review the Security Audits panel on this page before installing in production.

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