This document explains the design for storage and retrieval of the Field Level Lineage information.
Example: Consider a sample pipeline which performs join between HR dataset and Person dataset. The joined data is then normalized(to rename some fields, drop some fields, and create ID) before storing data into the data lake. Following diagram explains the sample pipeline which is reading from 2 file sources. Note that the 2D boxes represent the SCHEMA that is flowing through the pipeline while 3D boxes represents the pipeline stages.
In the lineage view, we show high level information first as shown below. Note that 'HR File', 'Person File', and 'Employee Data' are name of the input and output datasets, as indicated by the Reference name in the plugin properties.
Next detail level view contains the clickable fields from the input and output datasets. Note that 2D boxes represents fields belonging to the datasets. Since input datasets are of type file which does not have schema yet, plugin can provide any String name for it. In this case we are using "HR Record" and "Person Record" as name.
Once user clicks on particular field, field level lineage graph can be displayed.
Example: Graph for field ID, where circle represents the fields and edges represents operations with names in bubbles.
Note that "body" field is generated from "HR Record" as well as "Person Record". To distinguish it while storing we might need to prefix it with the stage name.
As an additional information for the source and target datasets we might want to show the associated properties such as file path, regex used etc.
Store:
Based on the above example, we want following pieces of information to be stored in the "FieldLevelLineage" dataset
- Properties associated with the Dataset. For example: File path, name of the directory associated with the "HR File", Broker Id, Topic name etc associated with the Kafka plugin. This will be single row per dataset per namespace per run of the pipeline.
- Fields associated with the Dataset. This will be single row per dataset per namespace per run of the pipeline. We will store each field as a separate column in this row.
- Lineage information associated with the target dataset. For each target dataset per run there will be single row which will contain the entire lineage graph.
Example: With one run of the pipeline shown above, following will be the sample data in the store.
| Row Key | Column Key | Value | Note |
|---|---|---|---|
| MyNamespace:HRFile:<runidX-inverted-start-time>:runidX | Properties | inputDir=/data/2017/hr regex=*.csv failOnError=false | One Row per namespace per dataset per run |
| MyNamespace: PersonFile:<runidX-inverted-start-time>:runidX | Properties | inputDir=/data/2017/person regex=*.csv failOnError=false | One Row per namespace per dataset per run |
| MyNamespace:EmployeeData:<runidX-inverted-start-time>:runidX | Properties | rowid=ID /*should we store schema too? what if that changes per run?*/ | One Row per namespace per dataset per run |
| MyNamespace:EmployeeData:AllFields:<runidX-inverted-start-time>:runidX | ID | /* We may not necessarily required to store any value*/ created_time:12345678 updated_time:12345678 last_updated_by:runid_X | One Row per namespace per dataset per run |
| MyNamespace:EmployeeData:AllFields:<runidX-inverted-start-time>:runidX | Name | ||
| MyNamespace:EmployeeData:AllFields:<runidX-inverted-start-time>:runidX | Department | ||
| MyNamespace:EmployeeData:AllFields:<runidX-inverted-start-time>:runidX | ContactDetails | ||
| MyNamespace:EmployeeData:AllFields:<runidX-inverted-start-time>:runidX | JoiningDate | ||
| MyNamespace:EmployeeData:<runidX-inverted-start-time>:runidX | Lineage | JSON representation of the LineageGraph provided by app to the platform.
| One row per run per target dataset |
JSON stored for ID field:
{
"sources": [
{
"name": "PersonFile",
"properties": {
"inputPath": "/data/2017/persons",
"regex": "*.csv"
}
},
{
"name": "HRFile",
"properties": {
"inputPath": "/data/2017/hr",
"regex": "*.csv"
}
}
],
"targets": [
{
"name": "Employee Data"
}
],
"operations": [
{
"inputs": [
{
"name": "PersonRecord",
"properties": {
"source": "PersonFile"
}
}
],
"outputs": [
{
"name": "body"
}
],
"name": "READ",
"description": "Read Person file.",
"properties": {
"stage": "Person File Reader"
}
},
{
"inputs": [
{
"name": "body"
}
],
"outputs": [
{
"name": "SSN"
}
],
"name": "PARSE",
"description": "Parse the body field",
"properties": {
"stage": "Person File Parser"
}
},
{
"inputs": [
{
"name": "HRRecord",
"properties": {
"source": "HRFile"
}
}
],
"outputs": [
{
"name": "body"
}
],
"name": "READ",
"description": "Read HR file.",
"properties": {
"stage": "HR File Reader"
}
},
{
"inputs": [
{
"name": "body"
}
],
"outputs": [
{
"name": "Employee_Name"
},
{
"name": "Dept_Name"
}
],
"name": "PARSE",
"description": "Parse the body field",
"properties": {
"stage": "HR File Parser"
}
},
{
"inputs": [
{
"name": "Employee_Name"
},
{
"name": "Dept_Name"
},
{
"name": "SSN"
}
],
"outputs": [
{
"name": "ID",
"properties": {
"target": "Employee Data"
}
}
],
"name": "GenerateID",
"description": "Generate unique Employee Id",
"properties": {
"stage": "Field Normalizer"
}
}
]
}
Few things to note:
- When platform receives the LineageGraph from the app, processing of the graph would be done before storing the data so the retrieval is straightforward.
- In the above pipeline, "HR File Parser" stage parses the body and generate fields "Employee_Name", "Dept_Name", "Salary", and "Start_Date". However the actual JSON stored for the ID field only contains operation related to the "Employee_Name" and "Dept_Name" since these are the only fields involved in the "ID" generation and not "Salary" and "Start_Date".
Retrieval:
Following REST APIs are available:
Get the list of fields in the dataset.
GET /v3/namespaces/<namespace-id>/datasets/<dataset-id>/fields?start=<start-ts>&end=<end-ts> Where: namespace-id: namespace name dataset-id: dataset name start-ts: starting timestamp(inclusive) in seconds end-ts: ending timestamp(exclusive) in seconds for lineage Sample Response: [ { "name": "ID", "properties": { "creation_time": 12345678, "last_update_time": 12345688, "last_modified_run": "runid_x" } }, { "name": "name", "properties": { "creation_time": 12345678, "last_update_time": 12345688, "last_modified_run": "runid_x" } }, { "name": "Department", "properties": { "creation_time": 12345678, "last_update_time": 12345688, "last_modified_run": "runid_x" } }, { "name": "ContactDetails", "properties": { "creation_time": 12345678, "last_update_time": 12345688, "last_modified_run": "runid_x" } }, { "name": "JoiningDate", "properties": { "creation_time": 12345678, "last_update_time": 12345688, "last_modified_run": "runid_x" } } ]Get the properties associated with the dataset.
GET /v3/namespaces/<namespace-id>/datasets/<dataset-id>/properties?start=<start-ts>&end=<end-ts> Where: namespace-id: namespace name dataset-id: dataset name start-ts: starting timestamp(inclusive) in seconds end-ts: ending timestamp(exclusive) in seconds for lineage Sample Response: [ { "programRun": "run1", "properties": { "inputPath": "/data/2017/hr", "regex": "*.csv" } }, { "programRun": "run2", "properties": { "inputPath": "/data/2017/anotherhrdata", "regex": "*.csv" } } ]Get the lineage associated with the particular field in a dataset.
GET /v3/namespaces/<namespace-id>/datasets/<dataset-id>/fields/<field-name>/lineage?start=<start-ts>&end=<end-ts> Where: namespace-id: namespace name dataset-id: dataset name field-name: name of the field for which lineage information to be retrieved start-ts: starting timestamp(inclusive) in seconds end-ts: ending timestamp(exclusive) in seconds for lineage
Sample response:
{ "startTimeInSeconds": 1442863938, "endTimeInSeconds": 1442881938, "paths": [ .... list of paths which represent the different ways field is created .... ] } Each path will look as follows: { "sources": [ { "name": "PersonFile", "properties": { "inputPath": "/data/2017/persons", "regex": "*.csv" } }, { "name": "HRFile", "properties": { "inputPath": "/data/2017/hr", "regex": "*.csv" } } ], "targets": [ { "name": "Employee Data" } ], "operations": [ { "inputs": [ { "name": "PersonRecord", "properties": { "source": "PersonFile" } } ], "outputs": [ { "name": "body" } ], "name": "READ", "description": "Read Person file.", "properties": { "stage": "Person File Reader" } }, { "inputs": [ { "name": "body" } ], "outputs": [ { "name": "SSN" } ], "name": "PARSE", "description": "Parse the body field", "properties": { "stage": "Person File Parser" } }, { "inputs": [ { "name": "HRRecord", "properties": { "source": "HRFile" } } ], "outputs": [ { "name": "body" } ], "name": "READ", "description": "Read HR file.", "properties": { "stage": "HR File Reader" } }, { "inputs": [ { "name": "body" } ], "outputs": [ { "name": "Employee_Name" }, { "name": "Dept_Name" } ], "name": "PARSE", "description": "Parse the body field", "properties": { "stage": "HR File Parser" } }, { "inputs": [ { "name": "Employee_Name" }, { "name": "Dept_Name" }, { "name": "SSN" } ], "outputs": [ { "name": "ID", "properties": { "target": "Employee Data" } } ], "name": "GenerateID", "description": "Generate unique Employee Id", "properties": { "stage": "Field Normalizer" } } ], "runs": [ "runidX", "runidY", "runidZ" ] }