TCP Connector
8 minute read
Overview
The TCP connector receives logs, metrics, and telemetry data sent via TCP protocol streams from applications, systems, and network devices. TCP (Transmission Control Protocol) provides reliable, ordered, and error-checked delivery of data over IP networks, ensuring no messages are lost in transit. Content streams into Edge Delta Pipelines for analysis by AI teammates through the Edge Delta MCP connector.
The connector acts as a TCP server, accepting persistent connections from multiple clients and processing incoming data streams with configurable message delimiters. It supports TLS encryption for secure transmission, connection limits for resource management, and custom tagging for metadata enrichment.
When you add this streaming connector, it appears as a TCP source in your selected pipeline. AI teammates access this data by querying the Edge Delta backend with the Edge Delta MCP connector.
For comprehensive TCP source configuration details, see the TCP Source documentation.
Add the TCP Connector
To add the TCP connector, you configure Edge Delta to listen for incoming TCP connections on a specified port, then configure applications to send data to that endpoint.
Prerequisites
Before configuring the connector, ensure you have:
- Edge Delta agent with network access to receive inbound connections
- Firewall rules allowing inbound TCP traffic on chosen port
- Applications or systems ready to send data via TCP
- For ports below 1024, root/admin privileges (or use ports 1024+)
Configuration Steps
- Navigate to AI Team > Connectors in the Edge Delta application
- Find the TCP connector in Streaming Connectors
- Click the connector card
- Configure Port (8080 default or custom)
- Configure Listen address (default
0.0.0.0
) - Configure Read Timeout (how long to wait for data)
- Optionally configure Advanced Settings for TLS, max connections, custom tags
- Select a target environment
- Click Save
The connector deploys and begins listening for TCP connections.

Configuration Options
Connector Name
Name to identify this TCP connector instance.
Port
TCP port to listen on for incoming connections.
Format: Integer between 1 and 65535
Default: 8080
Examples:
8080
- Common application port5140
- Custom log collection port9514
- Alternative high port (non-privileged)
Note: Ports below 1024 require root/admin privileges on Linux/Unix systems
Listen
IP address to bind to for listening.
Format: IPv4 address
Default: 0.0.0.0
(all interfaces)
Examples:
0.0.0.0
- Listen on all network interfaces192.168.1.100
- Listen only on specific interface10.0.0.50
- Bind to particular IP address
Read Timeout
How long to wait for incoming data before timing out connection.
Format: Duration (seconds, minutes)
Default: 1m
Examples:
30s
- 30 seconds for responsive connections1m
- 1 minute typical timeout5m
- 5 minutes for slow or sparse streams
Purpose: Prevents idle connections from holding resources indefinitely
Advanced Settings
Max Connections
Maximum number of concurrent TCP connections.
Format: Integer
Purpose: Limits resource usage when many clients connect simultaneously
When to Use: Set based on expected number of concurrent senders and available resources
Examples:
100
- Moderate number of clients500
- High-volume environments1000
- Large-scale deployments
TLS
TLS settings enable encrypted TCP connections for secure data transmission.
Configuration Options:
- Ignore Certificate Check: Disables SSL/TLS certificate verification (use with caution)
- CA File: Absolute file path to CA certificate for SSL/TLS
- CA Path: Absolute path where CA certificate files are located
- CRT File: Absolute path to SSL/TLS certificate file
- Key File: Absolute path to private key file for SSL/TLS
- Key Password: Optional password for private key file
- Client Auth Type: Client authentication type (default: noclientcert)
- Minimum Version: Minimum TLS version (default: TLSv1_2)
- Maximum Version: Maximum TLS version
Client Auth Types:
noclientcert
- No client certificate requestedrequestclientcert
- Client certificate requested but not requiredrequireanyclientcert
- Client certificate required but not validatedverifyclientcertifgiven
- Client certificate validated if providedrequireandverifyclientcert
- Client certificate required and validated
TLS Versions: TLSv1_0, TLSv1_1, TLSv1_2, TLSv1_3
When to Use: Enable for sensitive data (logs with PII, credentials, financial data), transmission over untrusted networks
Custom Tags
User-defined metadata tags to add to ingested data.
Fields:
- Name: Field name for custom tag
- Value: Field value (supports Go templating)
Available Template Fields:
{{.Source}}
- Data source identifier{{.SourceType}}
- Type of source{{.Tag}}
- Tag identifier{{.ConfigID}}
- Configuration ID{{.Host}}
- Host name{{.DockerContainerName}}
,{{.DockerImageName}}
- Docker-specific{{.K8sNamespace}}
,{{.K8sPodName}}
,{{.K8sContainerName}}
- Kubernetes-specific{{.ECSCluster}}
,{{.ECSTaskFamily}}
- ECS-specific{{.FileGlobPath}}
- File-specific
Examples:
- Name:
environment
, Value:production
- Name:
app
, Value:{{.K8sPodName}}
- Name:
region
, Value:us-east-1
Metadata Level (Resource Attributes)
This option is used to define which detected resources and attributes to add to each data item as it is ingested by Edge Delta. You can select:
- Required Only: This option includes the minimum required resources and attributes for Edge Delta to operate.
- Default: This option includes the required resources and attributes plus those selected by Edge Delta
- High: This option includes the required resources and attributes along with a larger selection of common optional fields.
- Custom: With this option selected, you can choose which attributes and resources to include. The required fields are selected by default and can’t be unchecked.
Based on your selection in the GUI, the source_metadata
YAML is populated as two dictionaries (resource_attributes
and attributes
) with Boolean values.
See Choose Data Item Metadata for more information on selecting metadata.
TCP-specific metadata included:
- Host name - Edge Delta agent hostname
- Host IP - Edge Delta agent IP address
- Server port - Port connector is listening on
- Service name - Service identifier
- Source name - Connector instance name
- Source type - TCP connector type
Metadata Level (Attributes)
Additional attribute-level metadata fields to include.
Default: ed.env.id
Rate Limit
Rate limit configuration to control volume of TCP data accepted.
Purpose: Protects Edge Delta from being overwhelmed by excessive connection rates or data volumes
Configuration:
- Evaluation Policy: Policy to decide whether source will be rate limited
When to Use: Prevent high-volume clients from overwhelming the pipeline or enforce data volume quotas
How to Use the TCP Connector
The TCP connector integrates seamlessly with AI Team, enabling data ingestion from TCP-capable sources. AI teammates automatically leverage TCP-ingested data to analyze application logs, investigate errors, monitor custom metrics, and track real-time events from distributed systems.
Use Case: Application Log Streaming
Collect logs from applications using TCP logging libraries (log4j, logback, winston) for real-time error detection. Applications send logs via TCP ensuring guaranteed delivery. AI teammates analyze application errors, identify patterns, and provide troubleshooting insights without manual log searching.
Configuration:
- Port:
5140
- Listen:
0.0.0.0
- Read Timeout:
1m
Application Configuration (Python):
import socket
import logging
# Configure logging to send to TCP
handler = logging.handlers.SocketHandler('edge-delta-host', 5140)
logger.addHandler(handler)
logger.error("Application error occurred")
Use Case: Custom Metrics Collection
Ingest custom metrics from monitoring agents via TCP for performance analysis. Monitoring agents send metrics as newline-delimited JSON over TCP. AI teammates track performance trends, identify anomalies, and correlate metrics with infrastructure events.
Configuration:
- Port:
9514
- Listen:
0.0.0.0
- Read Timeout:
30s
- Custom Tags:
environment: production
Metrics Sender (Shell):
echo '{"metric":"cpu_usage","value":75.3,"timestamp":1696176000}' | nc edge-delta-host 9514
Use Case: Centralized Log Aggregation
Aggregate logs from multiple servers using rsyslog or fluentd forwarding via TCP. Servers forward logs to Edge Delta TCP endpoint for centralized analysis. AI teammates provide unified visibility across distributed infrastructure, detecting issues spanning multiple servers.
Configuration:
- Port:
5140
- Listen:
0.0.0.0
- Read Timeout:
2m
- TLS: Enabled
rsyslog Configuration:
*.* @@edge-delta-host:5140
Troubleshooting
Connection refused errors: Verify Edge Delta listening on port with netstat -tln | grep 8080
. Check connector deployed to target environment. Test connectivity with telnet edge-delta-host 8080
. Review firewall rules allow inbound TCP traffic on configured port. Ensure listen address set to 0.0.0.0
for remote connections.
Clients connect but no data appears: Verify clients sending data after establishing connection. Check message delimiter matches client output format (newline vs custom). Review Edge Delta logs for parsing errors. Test with simple echo "test" | nc edge-delta-host 8080
. Ensure read timeout not too short for client send rate.
Connections timing out: Increase read timeout for clients sending sparse data. Verify clients sending data within timeout period. Check network latency between clients and Edge Delta. Monitor for connection resets due to network issues. Ensure clients maintaining persistent connections not repeatedly reconnecting.
TLS handshake failures: Verify TLS configuration includes certificate and private key files. Check certificate valid, not expired, includes correct hostname. Ensure clients configured for TLS connections. Verify TLS version compatibility between clients and server. Check client CA configured if using mutual TLS. Review SSL errors in both client and Edge Delta logs.
Max connections exceeded: Increase max connections limit based on number of concurrent clients. Monitor connection count and patterns. Check for connection leaks where clients don’t close properly. Deploy multiple Edge Delta agents and distribute client connections. Implement connection pooling on client side.
Messages split incorrectly: Verify message delimiter matches client output format. Newline delimiter works for line-oriented logs. Use custom delimiter for non-standard formats. Check clients not embedding delimiter characters within messages. Test with known good data to verify parsing.
High memory usage: Reduce max connections to limit concurrent clients. Check for very large messages causing memory spikes. Monitor message size distribution. Implement rate limiting to control data volume. Increase agent resources if consistently high load.
Next Steps
- Learn about TCP source configuration for advanced pipeline integration and detailed configuration options
- Learn about Edge Delta MCP connector for querying TCP data
- Learn about UDP connector for connectionless data ingestion
- Learn about creating custom teammates that can use TCP data
For additional help, visit AI Team Support.