Streaming CloudWatch Logs into OpenSearch Service - Overview.
Scope:
- Intro,
- The Concept of OpenSearch Patterns in the Context of CloudWatch Logs,
- OpenSearch relies on,
- Common OpenSearch Pattern Techniques for CloudWatch Logs,
- Sample Apache Access Log in CloudWatch Logs,
- Sample Apache Access Log In OpenSearch pipeline,
- Best Practices,
- Insights.
Intro:
- To stream Amazon CloudWatch Logs into OpenSearch Service, twtech can utilize a direct subscription filter or an intermediary AWS Lambda function.
- The subscription filter sends a real-time feed of log events to a destination, which can then ingest the data into twtech OpenSearch domain.
The Concept of OpenSearch Patterns in the Context of CloudWatch Logs
- When twtech streams CloudWatch Logs into Amazon OpenSearch Service (AOS) or OpenSearch self-managed, it often need a way to structure, parse, and query logs.
NB:
- Raw log data is often unstructured (JSON, plaintext, multiline errors),
OpenSearch relies on:
- Ingestion pipelines (via OpenSearch Ingest
Pipelines, Logstash, Fluent Bit, or Kinesis Data Firehose)
- Mappings & Index Templates (to define field types)
- Patterns (such as Grok patterns, regex, or JSON extraction)
- This pattern is used to turn raw log lines into structured fields that twtech can query, visualize (e.g., Kibana dashboards, OpenSearch Dashboards), or alert on.
Common OpenSearch Pattern
Techniques for CloudWatch Logs
1. JSON Logs
If twtech-app logs are already in
JSON (common with Lambda, ECS, EKS,
structured logging):
- CloudWatch → Firehose → OpenSearch can keep logs as
JSON.
- Use an index template in OpenSearch to define field
mappings.
- Example query in OpenSearch Dashboards:
{
"query": {
"match": { "level":
"ERROR" }
}
}
✅ Best for modern microservices
logging (fast parsing, structured search).
2. Grok Patterns (Regex-like
for Logs)
For plaintext logs (e.g., Apache,
NGINX, syslog), use Grok patterns to extract fields.
Sample Apache Access Log in
CloudWatch Logs
127.0.0.1
- frank [10/Oct/2025:13:55:36 -0700] "GET /index.html HTTP/1.0" 200
2326
Sample Apache Access Log In OpenSearch pipeline:
processors:
- grok:
field:
"message"
patterns:
-
'%{IPORHOST:clientip} %{USERNAME:ident} %{USERNAME:auth}
\[%{HTTPDATE:timestamp}\] "%{WORD:method} %{DATA:request}
HTTP/%{NUMBER:httpversion}" %{NUMBER:status} %{NUMBER:bytes}'
This extracts:
- clientip = 127.0.0.1
- method = GET
- request = /index.html
- status =
200
✅ Great for classic log formats, but
can be slower for very high-volume logs.
3. Regex & Scripted Fields
For custom formats, twtech can use regex
processors:
processors:
- dissect:
field: "message"
pattern: "%{clientip} - %{user}
[%{timestamp}] \"%{method} %{url} HTTP/%{version}\" %{status}
%{size}"
Or
use Painless scripts to
enrich data (e.g., convert epoch → timestamp).
4. Kinesis Data Firehose Transformations
When streaming CloudWatch Logs via
Firehose to OpenSearch:
- Firehose supports Lambda transformations.
- twtech can pre-parse logs into structured JSON before
ingestion.
- Example: Convert multi-line Java stack traces into
structured error logs.
5. Index Naming & Partitioning Patterns
Patterns aren’t just about parsing
logs — also about how you organize them:
- logs-<service>-<env>-YYYY.MM.DD → e.g., logs-app1-prod-2025.09.03
- Separate indices by log type (app, access,
error, security).
- Use ILM (Index Lifecycle Management) or ISM
(Index State Management in OpenSearch) to roll over and archive logs
efficiently.
6. Visualization & Search Patterns in OpenSearch Dashboards
Once logs are structured:
- Saved searches
→ e.g., status:[400 TO 599] for errors
- Aggregations
→ top 10 error endpoints
- Dashboards
→ latency percentiles, error trends, security alerts
- Alerting plugin
→ trigger notifications (Slack, SNS, PagerDuty)
Best Practices
- Prefer structured logging (JSON) over parsing plaintext → faster queries, lower ingestion cost.
- Use Grok sparingly
→ regex is powerful but CPU-expensive at scale.
- Normalize timestamps early (epoch, RFC3339) → makes queries & time filters consistent.
- Index wisely →
avoid dumping everything into one index. Use index patterns per
environment/service.
- Retention strategy →
hot (7–30 days) vs warm (archival in S3 via snapshot).
- Security → CloudWatch → Kinesis → OpenSearch should use IAM
roles and fine-grained access control.
Final thought:
OpenSearch patterns for CloudWatch Logs are all about turning unstructured
log streams into structured, queryable fields using Grok, regex, JSON
parsing, and ingestion pipelines — then organizing indices smartly for
performance and retention.
twtech-Insights:
- Hands-on with a realistic CloudWatch Logs event from a Lambda function into OpenSearch with an ingestion pipeline pattern that parses & indexes.
Step 1: Example
CloudWatch Logs Event from Lambda (JSON)
When Lambda writes to CloudWatch
Logs, it usually get JSON structured like this (after it’s decoded/unzipped from the CloudWatch stream):
{
"id": "12345678901234567890123456xxxx012",
"timestamp": 1693750xxxx000,
"message":
"{\"level\": \"ERROR\", \"function\":
\"user-service\", \"requestId\": \"abc-123\",
\"error\": \"UserNotFound\", \"details\":
{\"userId\": \"42\"}}"
}
Notice:
- CloudWatch wraps the Lambda log line in a JSON object
with id, timestamp, and message.
- The message field itself is a stringified JSON log line
from Lambda.
Step 2: Target Structure in
OpenSearch
- twtech wants to parse the nested JSON string in message so OpenSearch stores fields like this:
{
"id": "1234567890123456789012xxxx789012",
"timestamp":
"2023-09-03T00:00:00Z",
"level": "ERROR",
"function":
"user-service",
"requestId": "twtech-123",
"error": "UserNotFound",
"details.userId": "42"
}
Step 3: OpenSearch Ingestion
Pipeline
- Here’s a sample
ingest pipeline twtech defines in OpenSearch (via API or Dashboard → Stack Management → Ingest Pipelines):
PUT
_ingest/pipeline/cloudwatch-lambda-logs
{
"description": "twtech Parse
CloudWatch Lambda JSON logs",
"processors": [
{
"json": {
"field": "message",
"target_field":
"parsed_message",
"ignore_failure": true
}
},
{
"set": {
"field": "level",
"value":
"{{parsed_message.level}}",
"if":
"ctx.parsed_message != null"
}
},
{
"set": {
"field":
"function",
"value":
"{{parsed_message.function}}",
"if":
"ctx.parsed_message != null"
}
},
{
"set": {
"field":
"requestId",
"value":
"{{parsed_message.requestId}}",
"if": "ctx.parsed_message
!= null"
}
},
{
"set": {
"field": "error",
"value":
"{{parsed_message.error}}",
"if":
"ctx.parsed_message != null"
}
},
{
"rename": {
"field": "parsed_message.details.userId",
"target_field":
"userId",
"ignore_missing": true
}
},
{
"date": {
"field":
"timestamp",
"formats":
["UNIX_MS"] //
}
}
]
}
Step 4: Indexing Logs
with Pipeline
- When Firehose, Fluent Bit, or Logstash pushes documents into OpenSearch, twtech configures it to use this pipeline:
POST
cloudwatch-lambda-logs/_doc?pipeline=cloudwatch-lambda-logs
{
"id": "123456789012345678901xxxx6789012",
"timestamp": 169375xxxx000,
"message":
"{\"level\": \"ERROR\", \"function\":
\"user-service\", \"requestId\": \"abc-123\",
\"error\": \"UserNotFound\", \"details\":
{\"userId\": \"42\"}}"
}
Step 5: Final Indexed
Document
- After ingestion, OpenSearch stores it as:
{
"_index": "cloudwatch-lambda-logs",
"_id": "1234567890123456789012xxxx789012",
"_source": {
"id":
"1234567890123456789012xxxx789012",
"timestamp":
"2023-09-03T00:00:00Z",
"level": "ERROR",
"function":
"user-service",
"requestId": "abc-123",
"error":
"UserNotFound",
"userId": "42"
}
}
At this point, twtech can run queries
like:
{
"query": {
"match": { "error":
"UserNotFound" }
}
}
# Or
- Engineer may build dashboards for level: view ERROR trends.
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