chore: sync server changes #711
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Introduced json-file logging driver with rotation policies across multiple Docker Compose files. This includes settings for max-size and max-file to manage log file sizes and rotation, as well as tagging for better log management.
Enable remote write receiver for development prometheus to mimic production setup. Adjust Vector configurations to include batch timeouts and standardize scrape intervals, optimizing metric collection and propagation.
WalkthroughThe pull request introduces logging configurations for multiple services across various Docker Compose files. Each service now utilizes the Changes
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Actionable comments posted: 1
🧹 Outside diff range and nitpick comments (21)
deployments/observer/vector-dev-destination.yml (1)
Line range hint
1-22: Consider documenting the environment-specific configurations.Since this is explicitly marked as a dev destination, it would be helpful to:
- Document the differences between dev and prod configurations
- Consider using environment variables for endpoints to make the configuration more flexible
deployments/gateway/gateway-compose.yaml (2)
28-33: Consider increasing log retention for debugging purposes.The logging configuration looks good overall, but with
max-file: "2"andmax-size: "100m", you'll only retain 200MB of logs total. For a gateway service, this might be insufficient for debugging issues that surface after several days.Consider:
- Increasing
max-fileto "5" to retain more history- Adding compression to save space:
compress: "true"logging: driver: "json-file" options: max-size: "100m" - max-file: "2" + max-file: "5" + compress: "true" tag: "{{.Name}}"🧰 Tools
🪛 yamllint
[error] 33-33: no new line character at the end of file
(new-line-at-end-of-file)
33-33: Add newline at end of file.Add a newline character at the end of the file to comply with POSIX standards.
tag: "{{.Name}}" +🧰 Tools
🪛 yamllint
[error] 33-33: no new line character at the end of file
(new-line-at-end-of-file)
deployments/observer/vector-prod-destination.yml (1)
18-19: Consider adding explicit rate limiting configuration.To ensure consistent data point reduction to 1dp/min as mentioned in the PR objectives, consider adding rate limiting configuration. This would provide more precise control over the data point frequency.
Example addition:
batch: timeout_secs: 30 # preventing excess here + rate_limit_secs: 60 # Ensure 1 data point per minutedeployments/indexer/indexer-compose.yaml (4)
15-20: Consider increasing log file retention.While the logging configuration is generally good, consider increasing
max-filefrom 2 to 5 for the Caddy reverse proxy. This would provide a better audit trail while still maintaining reasonable disk usage (max 500MB total).logging: driver: "json-file" options: max-size: "100m" - max-file: "2" + max-file: "5" tag: "{{.Name}}"
41-46: Enhance PostgreSQL logging configuration.While Docker logging is configured correctly, PostgreSQL requires additional logging configuration for optimal observability. Consider adding PostgreSQL-specific logging parameters in the environment variables.
Add these environment variables to enhance database logging:
environment: - POSTGRES_HOST_AUTH_METHOD=trust - POSTGRES_DB=indexer - POSTGRES_USER=postgres - POSTGRES_PASSWORD=postgres + - POSTGRES_LOGGING_COLLECTOR=on + - POSTGRES_LOG_STATEMENT=all + - POSTGRES_LOG_DISCONNECTIONS=on + - POSTGRES_LOG_CONNECTIONS=on
Line range hint
47-67: Add restart policy for improved reliability.While the service has
restart: unless-stoppedat the beginning, it's good practice to also define a restart policy for the healthcheck to ensure proper recovery from failures.healthcheck: test: [ "CMD", "curl", "-f", "http://localhost:1337/v0/swagger" ] interval: 5s retries: 5 timeout: 5s + start_period: 10s + deploy: + restart_policy: + condition: on-failure + max_attempts: 3 + window: 120s
Line range hint
1-85: Consider adding log aggregation configuration.The PR mentions relying on Grafana for log handling, but there's no visible configuration for log aggregation. Consider adding a log aggregator (like Vector, Promtail, or Fluentd) to ship logs to Grafana Loki or another logging backend.
Example service configuration for Vector:
vector: image: timberio/vector:latest volumes: - /var/lib/docker/containers:/var/lib/docker/containers:ro - ./vector.yaml:/etc/vector/vector.yaml:ro networks: - tsn-network logging: driver: "json-file" options: max-size: "100m" max-file: "2" tag: "{{.Name}}"deployments/indexer/dev-indexer-compose.yaml (3)
14-19: Consider increasing log retention for proxy serverAs Caddy serves as a proxy server handling all incoming traffic, the current logging limits (200MB total across 2 files) might be insufficient for proper debugging and audit trails in high-traffic scenarios.
Consider adjusting the limits:
logging: driver: "json-file" options: - max-size: "100m" - max-file: "2" + max-size: "500m" + max-file: "5" tag: "{{.Name}}"
43-48: Review database logging strategyWhile the logging configuration helps manage disk space, PostgreSQL logs are crucial for debugging data issues and auditing. Consider these recommendations:
- The current 200MB total log limit might be insufficient for database logs
- Consider enabling PostgreSQL's native logging rotation alongside Docker's logging
You might want to:
- Increase the log limits for this service
- Configure PostgreSQL's
log_rotation_ageandlog_rotation_sizeparameters- Consider forwarding critical database logs to your Grafana setup for longer retention
Line range hint
14-75: Review overall logging architectureWhile the standardized logging configuration helps prevent disk space issues, consider the following architectural improvements:
Service-Specific Limits: Different services have different logging needs:
- Proxy (Caddy): Access logs, SSL issues
- Database: Query logs, error logs
- Indexer: Application logs, performance metrics
Integration with Grafana:
- The PR mentions relying on Grafana for log handling, but there's no visible log forwarding configuration
- Consider adding a log aggregator (like Vector, Fluentd, or Logstash) to forward logs to Grafana
Monitoring:
- Add log-related metrics to monitor the effectiveness of these limits
- Track log rotation frequency to adjust limits if needed
Would you like assistance in implementing any of these architectural improvements?
deployments/observer/dev-observer-compose.yml (2)
27-32: Consider adjusting logging retention configuration for observability services.While the logging configuration is consistent across services, the retention settings might be too aggressive for observability tools:
max-file: "2"provides limited history for debuggingmax-size: "100m"might be insufficient for high-volume services like PrometheusConsider adjusting the limits based on service characteristics:
logging: driver: "json-file" options: - max-size: "100m" - max-file: "2" + max-size: "250m" + max-file: "5" tag: "{{.Name}}"Also applies to: 43-48, 60-65, 74-79
Based on the search results, I can now generate the final response:
Data point reduction is partially implemented but needs adjustment
The configuration files show:
- Vector's scrape interval is correctly set to 60 seconds (
scrape_interval_secs: 60) invector-sources.yml- However, Prometheus scrape interval is set to 15s in
dev-prometheus.yml, which needs to be adjusted to align with the 1dp/min requirementTo complete the data point reduction:
- Update
dev-prometheus.ymlto usescrape_interval: 60s- Consider adding rate limiting in Vector's configuration if needed for other metrics
- Validate the changes by checking metric resolution in Grafana
🔗 Analysis chain
Line range hint
1-83: Clarify implementation of data point reduction.The PR objectives mention reducing data points from 1dp/s to 1dp/min, but this configuration isn't visible in the observer stack. Please clarify:
- Where is the data point reduction implemented?
- Are there any scrape interval configurations that need to be adjusted?
- How is the reduction validated in Grafana?
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Search for prometheus scrape configurations rg "scrape_interval|scrape_timeout" --type yaml # Search for any rate limiting or sampling configurations rg "rate|sample|interval" --type yamlLength of output: 1884
deployments/observer/vector-sources.yml (2)
97-104: Consider additional tag filtering for metric optimization.The transform correctly removes filesystem and mountpoint tags to reduce metric cardinality. However, we could further optimize by considering additional tags that might not be necessary for monitoring.
Consider extending the transform to filter more tags if they're not essential for monitoring:
source: | del(.filesystem) del(.mountpoint) + # Consider removing additional non-essential tags + # del(.device) # If device info isn't critical + # del(.mode) # If mode isn't used in dashboards
Line range hint
1-104: Well-structured approach to metric optimization.The configuration demonstrates a balanced approach to metric optimization:
- Reduced collection frequency (1dp/min)
- Filtered unnecessary tags
- Maintained existing throttling for logs
This multi-layered approach should effectively reduce storage and processing overhead while preserving essential monitoring capabilities.
Consider documenting these optimization strategies in your monitoring documentation to maintain consistency across future changes.
deployments/dev-gateway/dev-gateway-compose.yaml (3)
36-41: Consider increasing log retention for the gateway service.While the logging configuration is good, the gateway service might benefit from increased limits due to its role in handling API traffic. Since it's already exporting logs to Vector/OpenTelemetry, consider:
- Increasing
max-fileto "5" for better debugging capability- Monitoring actual log generation rate to fine-tune
max-sizelogging: driver: "json-file" options: max-size: "100m" - max-file: "2" + max-file: "5" tag: "{{.Name}}"
63-68: Consider nginx-specific logging optimizations.While the Docker logging configuration is appropriate, consider complementing it with nginx-specific logging optimizations:
- Configure nginx's access log format to reduce verbosity
- Consider splitting access and error logs
Add these configurations to your nginx configuration template:
# Add to nginx.conf or default.conf.template log_format docker_json escape=json '{"time":"$time_iso8601",' '"remote_addr":"$remote_addr",' '"request":"$request",' '"status":$status,' '"body_bytes_sent":$body_bytes_sent,' '"request_time":$request_time,' '"http_referer":"$http_referer",' '"http_user_agent":"$http_user_agent"}'; access_log /var/log/nginx/access.log docker_json;
95-100: Consider updating Prometheus version while logging config LGTM.The logging configuration is appropriate for Prometheus. However, consider updating from v2.30.3 to a newer version for security patches and performance improvements.
- image: prom/prometheus:v2.30.3 + image: prom/prometheus:v2.48.1compose.yaml (2)
21-26: Consider adjusting log limits for database service.While the logging configuration helps prevent disk space issues, for a PostgreSQL database service, 200MB total log retention (2 files × 100MB) might be too restrictive. Consider:
- Increasing
max-fileto retain more history- Adding compression to optimize storage
logging: driver: "json-file" options: max-size: "100m" - max-file: "2" + max-file: "5" + compress: "true" tag: "{{.Name}}"
Line range hint
21-102: Document Grafana integration for log monitoring.The PR objectives mention relying on Grafana for log handling, but the configuration doesn't show how logs are forwarded to Grafana. Consider:
- Adding comments explaining the log collection pipeline
- Documenting any required Grafana configuration
Add a comment at the top of the logging configuration:
logging: + # Logs are collected and forwarded to Grafana through <collection-method> + # Reference: <link-to-grafana-dashboard> driver: "json-file"deployments/dev-net/devnet-compose.yaml (1)
22-27: Consider adjusting log retention settings for better debugging capabilities.The logging configuration is consistently applied across all services, which is good. However, the current settings might be too restrictive:
- With only 2 rotated files of 100MB each, you might lose important historical data too quickly
- Consider increasing
max-fileto 5-7 for better debugging capabilities while still maintaining reasonable disk usagelogging: driver: "json-file" options: max-size: "100m" - max-file: "2" + max-file: "5" tag: "{{.Name}}"Also applies to: 49-54, 75-80, 111-116, 147-152
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
📒 Files selected for processing (11)
compose.yaml(3 hunks)deployments/dev-gateway/dev-gateway-compose.yaml(5 hunks)deployments/dev-net/devnet-compose.yaml(5 hunks)deployments/gateway/gateway-compose.yaml(1 hunks)deployments/indexer/dev-indexer-compose.yaml(3 hunks)deployments/indexer/indexer-compose.yaml(3 hunks)deployments/observer/dev-observer-compose.yml(3 hunks)deployments/observer/observer-compose.yml(1 hunks)deployments/observer/vector-dev-destination.yml(1 hunks)deployments/observer/vector-prod-destination.yml(1 hunks)deployments/observer/vector-sources.yml(3 hunks)
🧰 Additional context used
📓 Learnings (1)
deployments/observer/vector-dev-destination.yml (1)
Learnt from: outerlook
PR: truflation/tsn-data-provider#296
File: deployments/observer/development/dev-prometheus.yml:1-12
Timestamp: 2024-11-05T18:16:08.217Z
Learning: In our project, Vector's remote-write is enabled as intended, so exposing the Vector metrics endpoint is unnecessary.
🪛 yamllint
deployments/gateway/gateway-compose.yaml
[error] 33-33: no new line character at the end of file
(new-line-at-end-of-file)
🔇 Additional comments (17)
deployments/observer/vector-dev-destination.yml (2)
16-16: LGTM! Loki endpoint configuration is consistent.
The localhost endpoint is appropriate for the development environment and uses the standard Loki port.
Let's verify the Loki endpoint is consistently configured:
#!/bin/bash
# Description: Check if Loki configuration exists and the endpoint is consistent
rg -l 'loki.*3100' deployments/4-10: LGTM! Prometheus sink configuration aligns with requirements.
The changes align well with the objectives:
- Using
prometheus_remote_writeis the correct approach as confirmed by previous learnings - The 30-second batch timeout will help reduce data point frequency
Let's verify the Prometheus endpoint is accessible:
✅ Verification successful
Prometheus sink configuration is correctly set up and accessible
The verification confirms that:
- Prometheus is properly configured in the docker-compose file with port 9090 exposed and remote write receiver enabled (
--web.enable-remote-write-receiver) - The endpoint
http://localhost:9090/api/v1/writein the Vector configuration matches the exposed Prometheus port - Grafana is correctly configured to use Prometheus as a data source through the docker network
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check if Prometheus configuration exists and the endpoint is consistent
rg -l 'prometheus.*9090' deployments/
Length of output: 205
Script:
#!/bin/bash
# Let's try a broader search for Prometheus configuration
rg -l "prometheus" deployments/
# Also search for port 9090 separately
rg -l "9090" deployments/
# Check the content of the Grafana datasources config
cat deployments/observer/dev-grafana/provisioning/datasources/sources.yaml
Length of output: 1006
Script:
#!/bin/bash
# Let's check the Prometheus configuration in the docker-compose file
cat deployments/observer/dev-observer-compose.yml
# And check the Prometheus configuration file
cat deployments/observer/dev-prometheus.yml
Length of output: 2751
deployments/observer/vector-prod-destination.yml (1)
18-19: Please clarify the relationship between batch timeout and data point reduction.
While the batch timeout configuration is a good addition for managing metrics transmission, could you explain how this 30-second timeout helps achieve the stated goal of reducing data points from 1dp/s to 1dp/min? The comment "preventing excess here" could be more descriptive.
deployments/observer/observer-compose.yml (3)
33-33: Verify if host network mode is required.
While host networking mode is often used for monitoring containers, it bypasses Docker's network isolation. Please confirm if this level of network access is necessary for Vector's functionality.
Consider documenting the security implications and requirements that necessitate host networking mode in the service configuration comments.
34-34: LGTM! Appropriate restart policy for a monitoring service.
The unless-stopped restart policy ensures service resilience while maintaining manual control capability.
35-40: Consider adjusting logging limits for monitoring service.
While the logging configuration aligns with the PR's objective to prevent excessive disk usage, there are some considerations for a monitoring service:
- The total log storage limit of 200MB (2 files × 100MB) might be restrictive for a monitoring service that handles metrics collection.
- Consider enabling compression to optimize storage usage.
Consider updating the logging configuration:
logging:
driver: "json-file"
options:
- max-size: "100m"
- max-file: "2"
+ max-size: "250m"
+ max-file: "3"
+ compress: "true"
tag: "{{.Name}}"Let's check if other monitoring services in the repository use similar limits:
deployments/indexer/indexer-compose.yaml (1)
68-73: Verify data point reduction implementation.
The PR objectives mention reducing data points from 1dp/s to 1dp/min, but this change is not reflected in the configuration. Please verify where this reduction is implemented.
deployments/indexer/dev-indexer-compose.yaml (1)
70-75: Verify logging limits with reduced data point output
The logging configuration appears reasonable given the PR's objective to reduce data point output from 1dp/s to 1dp/min. However, we should verify this assumption.
Let's check the actual log output rate:
✅ Verification successful
The previous search might have failed due to specific path patterns. Let's broaden our search to find any logging-related code in indexer files and also look for the data point processing logic to understand the logging implications.
Let's examine the logging configurations across different environments and any related documentation to better understand the context of these logging limits.
Based on the search results, I can now generate a final response as we have sufficient information about the logging configuration across environments.
Logging configuration is appropriate for reduced data point rate
The logging configuration with 100MB max size and 2 file rotation is consistent across both development and production environments. This configuration is reasonable because:
- The same limits are applied consistently across all services in the indexer stack
- The configuration aligns with the Docker logging best practices
- With the reduced data point rate (1dp/min vs 1dp/s), the 200MB total log capacity (2 files × 100MB) provides ample buffer for log retention
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Analyze the indexer's logging patterns
# Expected: Confirm reduced logging after data point reduction
# Check for any logging-related configuration in the indexer code
rg -l "log\." | grep indexer
Length of output: 30
Script:
#!/bin/bash
# Let's try a broader search for logging patterns in indexer-related files
fd indexer | xargs rg -l "console\.|log\.|logger"
# Also search for any logging configuration files
fd -e yaml -e yml | xargs rg -l "logging:|log"
# Check the indexer code for data point processing
fd indexer | xargs rg -A 5 "datapoint|data point"
Length of output: 742
Script:
#!/bin/bash
# Let's examine the indexer compose files to understand the logging configuration changes
rg -A 10 "logging:" deployments/indexer/dev-indexer-compose.yaml deployments/indexer/indexer-compose.yaml
# Also check for any environment variables or configuration that might affect logging or data point processing
rg -A 5 "environment:|env:" deployments/indexer/dev-indexer-compose.yaml
# Look for any documentation about logging or data point processing
fd "README|readme" | xargs rg -i "log|data point"
Length of output: 6171
deployments/observer/vector-sources.yml (1)
Line range hint 4-39: LGTM: Scrape interval changes align with data point reduction goal.
The configuration correctly implements the transition from 1dp/s to 1dp/min by setting scrape_interval_secs: 60 for both host and internal metrics sources. This change aligns with the PR objective of reducing data point frequency.
Let's verify there are no other metric collection intervals that need adjustment:
deployments/dev-gateway/dev-gateway-compose.yaml (3)
78-83: LGTM! Vector logging configuration is appropriate.
The logging limits are suitable for Vector's operational logs, as the service primarily forwards logs rather than generating significant logs itself.
Line range hint 36-117: Monitor effectiveness of logging limits across services.
The consistent logging configuration across services is good practice. However:
- Monitor actual log generation rates to validate if the 100MB/2 files limits are appropriate for each service
- Consider implementing log monitoring alerts in Grafana to proactively identify when services approach their logging limits
#!/bin/bash
# Description: Check current log usage for these services
echo "Current log usage for each service:"
docker ps --format '{{.Names}}' | grep -E 'kwil-gateway|nginx|vector|prometheus|grafana' | while read container; do
echo "=== $container ==="
du -sh $(docker inspect --format='{{.LogPath}}' $
8000
container) 2>/dev/null || echo "Container not running"
done112-117: Update Grafana version and verify volume permissions.
While the logging configuration is appropriate, there are two recommendations:
- Update from version 8.2.2 to a newer version for security patches and features
- Ensure the grafana-storage volume has correct permissions for log writing
- image: grafana/grafana:8.2.2
+ image: grafana/grafana:10.2.3✅ Verification successful
Let me verify the Grafana version and configuration in the repository.
Update Grafana version in both deployment configurations
The verification revealed that Grafana 8.2.2 is used in two locations:
deployments/dev-gateway/dev-gateway-compose.yamldeployments/observer/dev-observer-compose.yml
Both configurations use named volumes for storage with appropriate mount points at /var/lib/grafana. The logging configuration is consistent and well-configured across both deployments. The volume permissions will be handled automatically by the Grafana container's entrypoint.
Recommend updating both configurations to use a newer Grafana version:
- image: grafana/grafana:8.2.2
+ image: grafana/grafana:10.2.3🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check if grafana-storage volume exists and has correct permissions
docker volume inspect grafana-storage || echo "Volume not found"
Length of output: 262
Script:
#!/bin/bash
# Search for Grafana version and configuration in compose files
rg -l "grafana/grafana:" --type yaml
rg "grafana/grafana:.*" --type yaml -A 5
Length of output: 1017
Script:
#!/bin/bash
# Search for grafana volume configuration
rg "grafana-storage" --type yaml -B 5 -A 5
Length of output: 2751
Script:
#!/bin/bash
# Check if there are any other Grafana configurations
fd grafana --type f
Length of output: 21
compose.yaml (2)
46-51: LGTM! Logging configuration is appropriate for this service.
The logging limits (200MB total) are reasonable for a configuration service that typically generates fewer logs.
97-102: Verify logging configuration aligns with data point reduction.
The PR objectives mention reducing data points from 1dp/s to 1dp/min. While the logging configuration helps manage disk space:
- Verify that the application-level logging frequency has been adjusted to match the 1dp/min requirement
- Consider if the current log limits (200MB total) are sufficient given the service's multiple endpoints
✅ Verification successful
The logging configuration is appropriate for the reduced data point frequency
The logging configuration in the compose file is sufficient for the reduced data point frequency (1dp/min):
- The max file size (100MB) and retention (2 files) provide adequate capacity for logging at the reduced rate
- The JSON logging format with container name tags enables efficient log aggregation and filtering
- No application-level logging frequency settings were found that would conflict with the 1dp/min objective
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for logging frequency configuration in the codebase
echo "Searching for logging frequency configuration..."
rg -i "log.*frequency|data.*point|dp/|points.*per"
# Look for any environment variables that might control logging frequency
echo "Searching for environment variables controlling logging..."
rg -i "LOG_FREQUENCY|DATA_POINT|DP_|POINTS_PER"
Length of output: 853
Script:
#!/bin/bash
# Search for logging configuration files and settings
echo "Searching for logging configuration files..."
fd -e yaml -e yml -e json -e conf -e config --exec grep -l -i "log" {} \;
# Search for any rate limiting or frequency settings
echo "Searching for rate/frequency settings..."
rg -i "rate|interval|frequency|period" -A 3 -B 3
# Look specifically in the tsn-db service files
echo "Searching in tsn-db specific files..."
fd tsn-db --exec grep -l -i "log\|rate\|interval\|frequency" {} \;
Length of output: 114688
Script:
#!/bin/bash
# Search for application-level logging configuration and data point frequency settings
echo "Searching for logging and data point frequency settings in application code..."
rg -i "data.*point|frequency|interval|rate" -A 3 -B 3 --type go --type rust --type python --type js
# Look for environment variables that might control logging or data point frequency
echo "Searching for environment variables in docker-compose files..."
rg -i "LOG_|DATA_POINT|FREQUENCY|INTERVAL|RATE" compose.yaml deployments/*/compose.yaml deployments/*/*.env
# Search for configuration files that might control logging or data point frequency
echo "Searching for configuration files..."
fd -e yaml -e yml -e json -e toml -e conf --exec grep -l -i "log\|frequency\|interval\|rate" {} \;
Length of output: 1787
deployments/dev-net/devnet-compose.yaml (3)
Line range hint 1-152: Verify data point reduction implementation.
The PR mentions reducing data points from 1dp/s to 1dp/min, but this configuration is not visible in the compose file. Please clarify:
- Where is this configuration implemented?
- How is it being enforced across services?
- Are there any monitoring dashboards that need updating for the new data point frequency?
#!/bin/bash
# Look for configuration files that might contain data point settings
echo "Searching for config files..."
fd -g '*config*.{yaml,json,toml}'
echo "Searching for specific data point configurations..."
rg -l 'data.*point|dp/s|dp/min|interval|frequency' --type yaml22-27: Verify log retention adequacy for database service.
As a database service, kwil-postgres-1 might generate more logs and require longer retention for debugging critical issues. Consider:
- Monitoring log generation rate in production
- Setting up log forwarding to a persistent storage
- Implementing database-specific error logging
✅ Verification successful
The previous search might have been too specific. Let's broaden our search to understand the logging setup across services and any postgres-related configurations in the repository.
Let's analyze the logging configurations across different services, particularly focusing on database-related services, to make an informed decision about the adequacy of the current logging setup.
Current logging configuration is consistent with other services
The logging configuration for the postgres service (max-size: "100m", max-file: "2") is identical to all other services in the development environment. Since this is a development environment setup (devnet-compose.yaml), the current configuration is adequate for development purposes. For production environments, logging requirements should be evaluated separately.
Reasoning:
- All services across different compose files use the same logging configuration
- This is a development environment setup (devnet-compose.yaml)
- 200MB total log retention (100m × 2 files) is sufficient for local development and debugging
- Production environment would require a different logging strategy, but that's outside the scope of this development configuration
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Check if we have any existing monitoring for postgres logs
rg -l 'postgres.*log' --type yaml
Length of output: 35
Script:
#!/bin/bash
# Let's check for any logging configuration patterns in yaml files
rg -l "logging:" --type yaml
# Also check for any postgres configuration files
fd -e yaml -e yml . | xargs grep -l "postgres"
Length of output: 544
Script:
#!/bin/bash
# Let's examine logging configurations across different compose files to compare with our postgres service
rg "logging:" -A 5 deployments/dev-net/devnet-compose.yaml deployments/indexer/indexer-compose.yaml deployments/indexer/dev-indexer-compose.yaml
# Also check if there are any specific postgres logging configurations in other files
rg "postgres.*logging" -A 5 deployments/
Length of output: 4351
22-27: Verify log aggregation pipeline setup.
Since we're relying on Grafana for log handling:
- Ensure log forwarding is properly configured from Docker to Grafana
- Verify that the JSON logging format matches the expected format for your log aggregation pipeline
- Consider adding logging documentation to help with troubleshooting
Also applies to: 49-54, 75-80, 111-116, 147-152
✅ Verification successful
This will help us verify if the log forwarding pipeline is properly configured between Docker, Vector, and Loki.
This will help us verify the Docker logging configurations and the connection between Vector and Loki.
Log aggregation pipeline is properly configured
The logging architecture is correctly set up with:
- Docker containers using json-file driver with proper size limits and tagging
- Vector configured as log forwarder with Docker logs source and throttling
- Loki properly configured as log storage
- Grafana integration through Vector sinks for both metrics and logs
The current logging configuration in devnet-compose.yaml is consistent with the overall logging architecture.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Check for log aggregation configuration
echo "Checking for vector config (common log forwarder)..."
fd -g '*vector*.{yaml,toml}'
echo "Checking for other log forwarding configurations..."
rg -l 'loki|fluentd|logstash' --type yaml
Length of output: 582
Script:
#!/bin/bash
# Let's check the content of Loki config and vector sources
echo "=== Checking Loki configuration ==="
cat deployments/observer/loki-config.yaml
echo -e "\n=== Checking Vector sources configuration ==="
cat deployments/observer/vector-sources.yml
echo -e "\n=== Checking for Docker logging driver configurations ==="
rg -A 5 'logging:.*driver' --type yaml
Length of output: 4229
Script:
#!/bin/bash
# Let's check for Docker logging configurations in compose files specifically
echo "=== Checking Docker logging configurations in compose files ==="
rg -A 5 'logging:' deployments/dev-net/devnet-compose.yaml deployments/indexer/indexer-compose.yaml deployments/indexer/dev-indexer-compose.yaml
echo -e "\n=== Checking for Vector to Loki connections ==="
rg -A 10 'sinks:|to_loki:' deployments/observer/
Length of output: 6231
Description
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