# ZephFlow Python SDK
[](https://pypi.org/project/zephflow/)
[](https://pypi.org/project/zephflow/)
[](https://opensource.org/licenses/Apache-2.0)
Python SDK for building and running ZephFlow data processing pipelines. ZephFlow provides a powerful, intuitive API for stream processing, data transformation, and event-driven architectures.
## Features
- **Simple, fluent API** for building data processing pipelines
- **Powerful filtering** using JSONPath expressions
- **Data transformation** with the eval expression language
- **Flow composition** - merge and combine multiple flows
- **Error handling** with assertions and error tracking
- **Multiple sink options** for outputting processed data
- **Java-based engine** for high performance processing
## Documentation
For comprehensive documentation, tutorials, and API reference, visit: [https://docs.fleak.ai/zephflow](https://docs.fleak.ai/zephflow)
## Prerequisites
- Python 3.8 or higher
- Java 17 or higher (required for the processing engine)
## Installation
Install ZephFlow using pip:
```bash
pip install zephflow
```
## Quick Start
Here's a simple example to get you started with ZephFlow:
```python
import zephflow
# Create a flow that filters and transforms events
flow = (
zephflow.ZephFlow.start_flow()
.filter("$.value > 10") # Keep only events with value > 10
.eval("""
dict(
id=$.id,
doubled_value=$.value * 2,
category=case(
$.value < 20 => 'medium',
_ => 'high'
)
)
""")
)
# Process some events
events = [
{"id": 1, "value": 5}, # Will be filtered out
{"id": 2, "value": 15}, # Will be processed
{"id": 3, "value": 25} # Will be processed
]
result = flow.process(events)
print(f"Processed {result.getOutputEvents().size()} events")
```
If you already have a workflow file:
```python
import zephflow
zephflow.ZephFlow.execute_dag("my_dag.yaml")
```
## Troubleshooting
### macOS SSL Certificate Issue
If you're on macOS and encounter an error like:
```<urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1007)>
This indicates that Python cannot verify SSL certificates due to missing system root certificates.
```
#### Solution
Run the certificate installation script that comes with your Python installation:
```
/Applications/Python\ 3.x/Install\ Certificates.command
Replace 3.x with your installed version (e.g., 3.10). This installs the necessary certificates so Python can verify HTTPS downloads.
```
## Core Concepts
### Filtering
Use JSONPath expressions to filter events:
```python
flow = (
zephflow.ZephFlow.start_flow()
.filter("$.priority == 'high' && $.value >= 100")
)
```
### Transformation
Transform data using the eval expression language:
```python
flow = (
zephflow.ZephFlow.start_flow()
.eval("""
dict(
timestamp=now(),
original_id=$.id,
processed_value=$.value * 1.1,
status='processed'
)
""")
)
```
### Merging Flows
Combine multiple flows for complex processing logic:
```python
high_priority = zephflow.ZephFlow.start_flow().filter("$.priority == 'high'")
large_value = zephflow.ZephFlow.start_flow().filter("$.value >= 1000")
merged = zephflow.ZephFlow.merge(high_priority, large_value)
```
### Error Handling
Add assertions to validate data and handle errors:
```python
flow = (
zephflow.ZephFlow.start_flow()
.assertion("$.required_field != null")
.assertion("$.value >= 0")
.eval("dict(id=$.id, validated_value=$.value)")
)
result = flow.process(events, include_error_by_step=True)
if result.getErrorByStep().size() > 0:
print("Some events failed validation")
```
## S3 Dead Letter Queue (DLQ)
ZephFlow supports automatic error handling by storing failed events to Amazon S3 using a Dead Letter Queue mechanism. **S3 DLQ works with data sources** (like file_source, kafka_source, etc.) and captures events that fail during **data ingestion, conversion, or pipeline processing** (including filter, assertion, eval failures).
### S3 DLQ Configuration with File Source
Configure S3 DLQ to automatically capture events that fail during data source processing:
```python
import tempfile
import json
import zephflow
from zephflow import JobContext, S3DlqConfig
# Create test data file with some invalid data
test_data = [
{"user_id": 1, "value": 100, "category": "A"},
{"user_id": 2, "value": 200, "category": "B"},
"invalid_json_string", # This will cause parsing failure -> DLQ
{"malformed": "json", "missing": 0 }, # This will cause parsing failure -> DLQ
]
# Write test data to file (including invalid JSON)
with tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False) as f:
for item in test_data:
if isinstance(item, dict):
f.write(json.dumps(item) + '\n')
else:
f.write(str(item) + '\n') # Write invalid JSON
input_file = f.name
# Configure S3 DLQ
dlq_config = S3DlqConfig(
region="us-west-2",
bucket="error-events-bucket",
batch_size=100, # Events to batch before writing
flush_interval_millis=30000, # Max wait time (30 seconds)
access_key_id="your-access-key",
secret_access_key="your-secret-key"
)
# Create JobContext with DLQ configuration
job_context = (
JobContext.builder()
.metric_tags({"env": "production", "service": "data-processor"})
.dlq_config(dlq_config)
.build()
)
# Create a flow with file source - DLQ will capture parsing failures
flow = (
zephflow.ZephFlow.start_flow(job_context)
.file_source(input_file, "JSON_OBJECT") # Invalid JSON lines will go to DLQ
.filter("$.value > 0") # Normal pipeline processing
.eval("""
dict(
user_id=$.user_id,
processed_value=$.value * 1.1,
processed_at=now()
)
""")
.stdout_sink("JSON_OBJECT")
)
# Execute the flow - source parsing failures will be sent to S3 DLQ
flow.execute("data-processor", "production", "json-processor")
print(f"Invalid JSON events sent to S3 DLQ: error-events-bucket")
# Cleanup
import os
os.unlink(input_file)
```
### S3 DLQ with Kafka Source
S3 DLQ also works with streaming sources like Kafka to capture deserialization failures:
```python
import zephflow
from zephflow import JobContext, S3DlqConfig
# Configure S3 DLQ for Kafka processing errors
dlq_config = S3DlqConfig(
region="us-east-1",
bucket="kafka-processing-errors",
batch_size=50,
flush_interval_millis=10000,
access_key_id="your-access-key",
secret_access_key="your-secret-key"
)
job_context = (
JobContext.builder()
.dlq_config(dlq_config)
.metric_tags({"env": "production", "service": "kafka-processor"})
.build()
)
# Kafka source with DLQ - will capture messages that fail JSON parsing
flow = (
zephflow.ZephFlow.start_flow(job_context)
.kafka_source(
broker="localhost:9092",
topic="user-events",
group_id="processor-group",
encoding_type="JSON_OBJECT" # Invalid JSON messages will go to DLQ
)
.filter("$.event_type == 'purchase'")
.eval("""
dict(
user_id=$.user_id,
amount=$.amount,
processed_at=now()
)
""")
.stdout_sink("JSON_OBJECT")
)
# This would run continuously, capturing Kafka deserialization failures to S3 DLQ
# flow.execute("kafka-processor", "production", "purchase-events")
```
### S3 DLQ with Pipeline Processing Failures
S3 DLQ also captures pipeline processing failures like assertion errors:
```python
import tempfile
import json
import zephflow
from zephflow import JobContext, S3DlqConfig
# Create test data with values that will cause assertion failures
test_data = [
{"user_id": 1, "value": 100, "category": "A"}, # Will pass
{"user_id": 2, "value": 1500, "category": "B"}, # Will fail assertion (> 1000) -> DLQ
{"user_id": 3, "value": 50, "category": "A"}, # Will pass
{"user_id": 4, "value": 2000, "category": "C"}, # Will fail assertion (> 1000) -> DLQ
]
# Write test data to file
with tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False) as f:
for item in test_data:
f.write(json.dumps(item) + '\n')
input_file = f.name
# Configure S3 DLQ
dlq_config = S3DlqConfig(
region="us-west-2",
bucket="pipeline-error-events",
batch_size=10,
flush_interval_millis=5000,
access_key_id="your-access-key",
secret_access_key="your-secret-key"
)
job_context = (
JobContext.builder()
.metric_tags({"env": "production", "service": "data-validator"})
.dlq_config(dlq_config)
.build()
)
# Pipeline with assertion that will cause some events to fail
flow = (
zephflow.ZephFlow.start_flow(job_context)
.file_source(input_file, "JSON_OBJECT")
.filter("$.value > 0") # Basic filtering
.assertion("$.value < 1000") # This will fail for value=1500,2000 -> DLQ
.eval("""
dict(
user_id=$.user_id,
validated_value=$.value,
processed_at=now()
)
""")
.stdout_sink("JSON_OBJECT")
)
# Execute - assertion failures will be sent to S3 DLQ
flow.execute("data-validator", "production", "validation-service")
print(f"Assertion failures sent to S3 DLQ: pipeline-error-events")
# Cleanup
import os
os.unlink(input_file)
```
### S3 DLQ Configuration Options
The `S3DlqConfig` supports the following parameters:
- `region`: AWS region where the DLQ bucket is located
- `bucket`: S3 bucket name for storing failed events
- `batch_size`: Number of events to batch before writing (default: 100)
- `flush_interval_millis`: Maximum time to wait before flushing events (default: 5000ms)
- `access_key_id`: AWS access key (optional, uses default credential chain if not provided)
- `secret_access_key`: AWS secret key (optional, uses default credential chain if not provided)
### DLQ Error Event Format
Failed source events are stored in S3 using Avro format with the following structure:
- **processingTimestamp**: Timestamp when the error occurred (milliseconds)
- **key**: Original message key (bytes, nullable)
- **value**: Original message value (bytes, nullable)
- **metadata**: Additional metadata about the source (map of strings, nullable)
- **errorMessage**: Error details including stack trace (string)
### Common S3 DLQ Use Cases
S3 DLQ captures failures when using **data sources**, including:
- **JSON parsing failures** in file_source or kafka_source
- **Deserialization errors** when converting raw data to structured format
- **Schema validation failures** at the source level
- **Network or I/O errors** during data fetching
- **Pipeline processing failures** like assertion failures, eval errors, or filter exceptions
- **Data transformation errors** in any pipeline step
**Note**: S3 DLQ **only works with data sources** (file_source, kafka_source, etc.). When using `flow.process(events)` with in-memory data, pipeline failures are handled through `result.getErrorByStep()` instead.
## Examples
For more detailed examples, check out [Quick Start Example](https://github.com/fleaktech/zephflow-python-sdk/blob/main/examples/quickstart.py) - Basic filtering and transformation
## Environment Variables
- `ZEPHFLOW_MAIN_JAR` - Path to a custom ZephFlow JAR file (optional)
- `ZEPHFLOW_JAR_DIR` - Directory for storing downloaded JAR files (optional)
## Support
- **Documentation**: [https://docs.fleak.ai/zephflow](https://docs.fleak.ai/zephflow)
- **Discussions**: [Slack](https://join.slack.com/t/fleak-hq/shared_invite/zt-361k9cnhf-9~mmjpOH1IbZfRxeXplfKA)
## License
This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.
## About Fleak
ZephFlow is developed and maintained by [Fleak Tech Inc.](https://fleak.ai), building the future of data processing and streaming analytics.
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"description": "# ZephFlow Python SDK\n\n[](https://pypi.org/project/zephflow/)\n[](https://pypi.org/project/zephflow/)\n[](https://opensource.org/licenses/Apache-2.0)\n\nPython SDK for building and running ZephFlow data processing pipelines. ZephFlow provides a powerful, intuitive API for stream processing, data transformation, and event-driven architectures.\n\n## Features\n\n- **Simple, fluent API** for building data processing pipelines\n- **Powerful filtering** using JSONPath expressions\n- **Data transformation** with the eval expression language\n- **Flow composition** - merge and combine multiple flows\n- **Error handling** with assertions and error tracking\n- **Multiple sink options** for outputting processed data\n- **Java-based engine** for high performance processing\n\n## Documentation\n\nFor comprehensive documentation, tutorials, and API reference, visit: [https://docs.fleak.ai/zephflow](https://docs.fleak.ai/zephflow)\n\n## Prerequisites\n\n- Python 3.8 or higher\n- Java 17 or higher (required for the processing engine)\n\n## Installation\n\nInstall ZephFlow using pip:\n\n```bash\npip install zephflow\n```\n\n## Quick Start\n\nHere's a simple example to get you started with ZephFlow:\n\n```python\nimport zephflow\n\n# Create a flow that filters and transforms events\nflow = (\n zephflow.ZephFlow.start_flow()\n .filter(\"$.value > 10\") # Keep only events with value > 10\n .eval(\"\"\"\n dict(\n id=$.id,\n doubled_value=$.value * 2,\n category=case(\n $.value < 20 => 'medium',\n _ => 'high'\n )\n )\n \"\"\")\n)\n\n# Process some events\nevents = [\n {\"id\": 1, \"value\": 5}, # Will be filtered out\n {\"id\": 2, \"value\": 15}, # Will be processed\n {\"id\": 3, \"value\": 25} # Will be processed\n]\n\nresult = flow.process(events)\nprint(f\"Processed {result.getOutputEvents().size()} events\")\n```\n\nIf you already have a workflow file:\n\n```python\nimport zephflow\n\nzephflow.ZephFlow.execute_dag(\"my_dag.yaml\")\n```\n\n## Troubleshooting\n### macOS SSL Certificate Issue\nIf you're on macOS and encounter an error like:\n\n```<urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1007)>\nThis indicates that Python cannot verify SSL certificates due to missing system root certificates.\n```\n\n#### Solution\nRun the certificate installation script that comes with your Python installation:\n\n```\n/Applications/Python\\ 3.x/Install\\ Certificates.command\nReplace 3.x with your installed version (e.g., 3.10). This installs the necessary certificates so Python can verify HTTPS downloads.\n```\n\n## Core Concepts\n\n### Filtering\n\nUse JSONPath expressions to filter events:\n\n```python\nflow = (\n zephflow.ZephFlow.start_flow()\n .filter(\"$.priority == 'high' && $.value >= 100\")\n)\n```\n\n### Transformation\n\nTransform data using the eval expression language:\n\n```python\nflow = (\n zephflow.ZephFlow.start_flow()\n .eval(\"\"\"\n dict(\n timestamp=now(),\n original_id=$.id,\n processed_value=$.value * 1.1,\n status='processed'\n )\n \"\"\")\n)\n```\n\n### Merging Flows\n\nCombine multiple flows for complex processing logic:\n\n```python\nhigh_priority = zephflow.ZephFlow.start_flow().filter(\"$.priority == 'high'\")\nlarge_value = zephflow.ZephFlow.start_flow().filter(\"$.value >= 1000\")\n\nmerged = zephflow.ZephFlow.merge(high_priority, large_value)\n```\n\n### Error Handling\n\nAdd assertions to validate data and handle errors:\n\n```python\nflow = (\n zephflow.ZephFlow.start_flow()\n .assertion(\"$.required_field != null\")\n .assertion(\"$.value >= 0\")\n .eval(\"dict(id=$.id, validated_value=$.value)\")\n)\n\nresult = flow.process(events, include_error_by_step=True)\nif result.getErrorByStep().size() > 0:\n print(\"Some events failed validation\")\n```\n\n## S3 Dead Letter Queue (DLQ)\n\nZephFlow supports automatic error handling by storing failed events to Amazon S3 using a Dead Letter Queue mechanism. **S3 DLQ works with data sources** (like file_source, kafka_source, etc.) and captures events that fail during **data ingestion, conversion, or pipeline processing** (including filter, assertion, eval failures).\n\n### S3 DLQ Configuration with File Source\n\nConfigure S3 DLQ to automatically capture events that fail during data source processing:\n\n```python\nimport tempfile\nimport json\nimport zephflow\nfrom zephflow import JobContext, S3DlqConfig\n\n# Create test data file with some invalid data\ntest_data = [\n {\"user_id\": 1, \"value\": 100, \"category\": \"A\"},\n {\"user_id\": 2, \"value\": 200, \"category\": \"B\"},\n \"invalid_json_string\", # This will cause parsing failure -> DLQ\n {\"malformed\": \"json\", \"missing\": 0 }, # This will cause parsing failure -> DLQ\n]\n\n# Write test data to file (including invalid JSON)\nwith tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False) as f:\n for item in test_data:\n if isinstance(item, dict):\n f.write(json.dumps(item) + '\\n')\n else:\n f.write(str(item) + '\\n') # Write invalid JSON\n input_file = f.name\n\n# Configure S3 DLQ\ndlq_config = S3DlqConfig(\n region=\"us-west-2\",\n bucket=\"error-events-bucket\",\n batch_size=100, # Events to batch before writing\n flush_interval_millis=30000, # Max wait time (30 seconds)\n access_key_id=\"your-access-key\",\n secret_access_key=\"your-secret-key\"\n)\n\n# Create JobContext with DLQ configuration\njob_context = (\n JobContext.builder()\n .metric_tags({\"env\": \"production\", \"service\": \"data-processor\"})\n .dlq_config(dlq_config)\n .build()\n)\n\n# Create a flow with file source - DLQ will capture parsing failures\nflow = (\n zephflow.ZephFlow.start_flow(job_context)\n .file_source(input_file, \"JSON_OBJECT\") # Invalid JSON lines will go to DLQ\n .filter(\"$.value > 0\") # Normal pipeline processing\n .eval(\"\"\"\n dict(\n user_id=$.user_id,\n processed_value=$.value * 1.1,\n processed_at=now()\n )\n \"\"\")\n .stdout_sink(\"JSON_OBJECT\")\n)\n\n# Execute the flow - source parsing failures will be sent to S3 DLQ\nflow.execute(\"data-processor\", \"production\", \"json-processor\")\nprint(f\"Invalid JSON events sent to S3 DLQ: error-events-bucket\")\n\n# Cleanup\nimport os\nos.unlink(input_file)\n```\n\n### S3 DLQ with Kafka Source\n\nS3 DLQ also works with streaming sources like Kafka to capture deserialization failures:\n\n```python\nimport zephflow\nfrom zephflow import JobContext, S3DlqConfig\n\n# Configure S3 DLQ for Kafka processing errors\ndlq_config = S3DlqConfig(\n region=\"us-east-1\",\n bucket=\"kafka-processing-errors\",\n batch_size=50,\n flush_interval_millis=10000,\n access_key_id=\"your-access-key\",\n secret_access_key=\"your-secret-key\"\n)\n\njob_context = (\n JobContext.builder()\n .dlq_config(dlq_config)\n .metric_tags({\"env\": \"production\", \"service\": \"kafka-processor\"})\n .build()\n)\n\n# Kafka source with DLQ - will capture messages that fail JSON parsing\nflow = (\n zephflow.ZephFlow.start_flow(job_context)\n .kafka_source(\n broker=\"localhost:9092\",\n topic=\"user-events\",\n group_id=\"processor-group\",\n encoding_type=\"JSON_OBJECT\" # Invalid JSON messages will go to DLQ\n )\n .filter(\"$.event_type == 'purchase'\")\n .eval(\"\"\"\n dict(\n user_id=$.user_id,\n amount=$.amount,\n processed_at=now()\n )\n \"\"\")\n .stdout_sink(\"JSON_OBJECT\")\n)\n\n# This would run continuously, capturing Kafka deserialization failures to S3 DLQ\n# flow.execute(\"kafka-processor\", \"production\", \"purchase-events\")\n```\n\n### S3 DLQ with Pipeline Processing Failures\n\nS3 DLQ also captures pipeline processing failures like assertion errors:\n\n```python\nimport tempfile\nimport json\nimport zephflow\nfrom zephflow import JobContext, S3DlqConfig\n\n# Create test data with values that will cause assertion failures\ntest_data = [\n {\"user_id\": 1, \"value\": 100, \"category\": \"A\"}, # Will pass\n {\"user_id\": 2, \"value\": 1500, \"category\": \"B\"}, # Will fail assertion (> 1000) -> DLQ\n {\"user_id\": 3, \"value\": 50, \"category\": \"A\"}, # Will pass\n {\"user_id\": 4, \"value\": 2000, \"category\": \"C\"}, # Will fail assertion (> 1000) -> DLQ\n]\n\n# Write test data to file\nwith tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False) as f:\n for item in test_data:\n f.write(json.dumps(item) + '\\n')\n input_file = f.name\n\n# Configure S3 DLQ\ndlq_config = S3DlqConfig(\n region=\"us-west-2\",\n bucket=\"pipeline-error-events\",\n batch_size=10,\n flush_interval_millis=5000,\n access_key_id=\"your-access-key\",\n secret_access_key=\"your-secret-key\"\n)\n\njob_context = (\n JobContext.builder()\n .metric_tags({\"env\": \"production\", \"service\": \"data-validator\"})\n .dlq_config(dlq_config)\n .build()\n)\n\n# Pipeline with assertion that will cause some events to fail\nflow = (\n zephflow.ZephFlow.start_flow(job_context)\n .file_source(input_file, \"JSON_OBJECT\")\n .filter(\"$.value > 0\") # Basic filtering\n .assertion(\"$.value < 1000\") # This will fail for value=1500,2000 -> DLQ\n .eval(\"\"\"\n dict(\n user_id=$.user_id,\n validated_value=$.value,\n processed_at=now()\n )\n \"\"\")\n .stdout_sink(\"JSON_OBJECT\")\n)\n\n# Execute - assertion failures will be sent to S3 DLQ\nflow.execute(\"data-validator\", \"production\", \"validation-service\")\nprint(f\"Assertion failures sent to S3 DLQ: pipeline-error-events\")\n\n# Cleanup\nimport os\nos.unlink(input_file)\n```\n\n### S3 DLQ Configuration Options\n\nThe `S3DlqConfig` supports the following parameters:\n\n- `region`: AWS region where the DLQ bucket is located\n- `bucket`: S3 bucket name for storing failed events\n- `batch_size`: Number of events to batch before writing (default: 100)\n- `flush_interval_millis`: Maximum time to wait before flushing events (default: 5000ms)\n- `access_key_id`: AWS access key (optional, uses default credential chain if not provided)\n- `secret_access_key`: AWS secret key (optional, uses default credential chain if not provided)\n\n### DLQ Error Event Format\n\nFailed source events are stored in S3 using Avro format with the following structure:\n\n- **processingTimestamp**: Timestamp when the error occurred (milliseconds)\n- **key**: Original message key (bytes, nullable)\n- **value**: Original message value (bytes, nullable)\n- **metadata**: Additional metadata about the source (map of strings, nullable)\n- **errorMessage**: Error details including stack trace (string)\n\n### Common S3 DLQ Use Cases\n\nS3 DLQ captures failures when using **data sources**, including:\n\n- **JSON parsing failures** in file_source or kafka_source\n- **Deserialization errors** when converting raw data to structured format\n- **Schema validation failures** at the source level\n- **Network or I/O errors** during data fetching\n- **Pipeline processing failures** like assertion failures, eval errors, or filter exceptions\n- **Data transformation errors** in any pipeline step\n\n**Note**: S3 DLQ **only works with data sources** (file_source, kafka_source, etc.). When using `flow.process(events)` with in-memory data, pipeline failures are handled through `result.getErrorByStep()` instead.\n\n## Examples\n\nFor more detailed examples, check out [Quick Start Example](https://github.com/fleaktech/zephflow-python-sdk/blob/main/examples/quickstart.py) - Basic filtering and transformation\n\n## Environment Variables\n\n- `ZEPHFLOW_MAIN_JAR` - Path to a custom ZephFlow JAR file (optional)\n- `ZEPHFLOW_JAR_DIR` - Directory for storing downloaded JAR files (optional)\n\n\n## Support\n\n- **Documentation**: [https://docs.fleak.ai/zephflow](https://docs.fleak.ai/zephflow)\n- **Discussions**: [Slack](https://join.slack.com/t/fleak-hq/shared_invite/zt-361k9cnhf-9~mmjpOH1IbZfRxeXplfKA)\n\n## License\n\nThis project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.\n\n## About Fleak\n\nZephFlow is developed and maintained by [Fleak Tech Inc.](https://fleak.ai), building the future of data processing and streaming analytics.\n\n",
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