FlowX.AI Data Search

The Data Search service enables you to find specific data across process instances within your FlowX.AI platform. It uses Elasticsearch to perform fast, indexed searches across process data.

Simple Search

Search by a single field and value

Advanced Search

Search using multiple criteria simultaneously

Array Search

Search within array elements and nested structures

Cross-Application

Search across multiple applications

Date Filtering

Filter results by process creation dates

Prerequisites

Before using the Data Search service, ensure you have:

Elasticsearch Configuration

Elasticsearch cluster running and accessible
Indexing enabled in FlowX Engine configuration
Proper network connectivity between services

Kafka Topics

KAFKA_TOPIC_DATA_SEARCH_IN configured for requests
KAFKA_TOPIC_DATA_SEARCH_OUT configured for responses
Proper topic permissions and access

Process Data

Process instances with indexed data
Searchable fields defined in Process Settings
Completed process instances for reliable results

Quick start

Set up your data process

Create a process that stores searchable data and configure field indexing.

Create a search process

Build a process with Kafka Send/Receive actions for search functionality.

Configure search parameters

Define your search criteria using simple, advanced, or array search syntax.

Handle search results

Process the returned data and display results to users.

1. Set up your data process

First, create a process that contains searchable data. Add a Service Task with a business rule:

output.put("application", {
  "client": {
    "personalInfo": {
      "firstName": "Sarah",
      "lastName": "Johnson", 
      "email": "sarah.johnson@techcorp.com",
      "phone": "+1-555-0101"
    },
    "address": {
      "country": "USA",
      "city": "San Francisco", 
      "state": "California",
      "zipCode": "94102"
    },
    "business": {
      "company": "TechCorp Inc",
      "industry": "Technology",
      "position": "CTO"
    },
    "account": {
      "type": "Premium",
      "status": "Active",
      "riskLevel": "Low"
    }
  }
});

Critical Step: After creating your data structure, you must configure field indexing in Process Settings → Data Search for the search to work.

Configure Field Indexing

Navigate to Process Settings → Data Search and add the field paths you want to search:

Fields that we indexed in our example:

application.client.personalInfo.firstName
application.client.personalInfo.lastName
application.client.personalInfo.email
application.listClient.[].firstName (for array search)
application.listClient.[].lastName (for array search)
application.listClient.[].department (for array search)

2. Create your search process

Create a new process with a Send Message Task:

Configuration:

Action Type: Kafka Send Action
Topic: KAFKA_TOPIC_DATA_SEARCH_IN

3. Configure search parameters

Choose your search approach based on your needs:

Simple Search
Advanced Search
Array Search
Cross-Application
Date Filtering

Use when searching by a single field:

{
  "searchKey": "application.client.personalInfo.lastName",
  "value": "Johnson",
  "processDefinitionNames": ["client_process"],
  "states": ["FINISHED"]
}

4. Handle search results

Add a Receive Message Task with:

Data Stream: KAFKA_TOPIC_DATA_SEARCH_OUT

Success response example:

{
  "result": [
    {
      "processInstanceUUID": "b743ac99-4029-4b93-9ac3-4a45b4a62673",
      "state": "FINISHED",
      "processStartDate": "2025-05-28T12:18:50.532Z",
      "data": {
        "application": {
          "client": {
            "personalInfo": {
              "firstName": "Sarah",
              "lastName": "Johnson"
            }
          }
        }
      }
    }
  ],
  "tooManyResults": false,
  "success": true
}

Search parameters reference

Quick reference table

Parameter	Type	Mandatory	Description	Example
`searchKey`	String	Yes	Single field path to search	`"application.client.personalInfo.lastName"`
`value`	String	Yes	Value to search for (with searchKey)	`"Johnson"`
`searchKeys`	Array	Yes (if multiple fields)	Multiple field-value pairs (AND logic)	`[{"key": "field1", "value": "val1"}]`
`searchByPaths`	Array	Yes (for array search)	Array search field-value pairs	`[{"key": "list.[].field", "value": "val"}]`
`processDefinitionNames`	Array	Yes	Limit to specific processes	`["client_onboarding"]`
`applicationIds`	Array	No	Search across applications	`["uuid-1", "uuid-2"]`
`states`	Array	No	Filter by process states	`["FINISHED", "STARTED"]`
`processStartDateAfter`	String	No	Include processes after date	`"2024-01-01T00:00:00Z"`
`processStartDateBefore`	String	No	Include processes before date	`"2024-12-31T23:59:59Z"`

Basic search parameters

searchKey (String)

Purpose: The field path to search in for single-field searches.Usage: Use dot notation to specify the exact path to the field you want to search or a specific key in the payload.Examples:

"application.client.personalInfo.firstName"
"application.client.address.city"
"application.client.business.industry"

Important: This field must be indexed in Process Settings → Data Search to work.

Cannot be used together with searchKeys or searchByPaths. Choose one approach.

value (String)

Purpose: The exact value to search for when using searchKey.Usage: Must match the stored value exactly (case-sensitive).Examples:

"Johnson" - searches for exact lastName match
"Technology" - searches for exact industry match
"Active" - searches for exact status match

Note: Only used with searchKey, not with searchKeys or searchByPaths.

searchKeys (Array)

Purpose: Array of key-value pairs for multi-field searches with AND logic.Usage: All conditions must match for a result to be returned. Can also be used for array searches with [] notation.Format:

[
  {"key": "field.path.1", "value": "value1"},
  {"key": "field.path.2", "value": "value2"}
]

Examples:

[
  {"key": "application.client.personalInfo.firstName", "value": "Sarah"},
  {"key": "application.client.business.industry", "value": "Technology"}
]

Array Search Example:

[
  {"key": "application.listClient.[].firstName", "value": "Maria"}
]

Cannot be used together with searchKey or searchByPaths. Choose one approach.

searchByPaths (Array)

Purpose: Array of key-value pairs specifically designed for searching within array elements and nested structures.Usage: Use [] notation to search within array elements. This searches through all items in the array and is the recommended approach for array searches.Format:

[
  {"key": "arrayfield.[].property", "value": "searchvalue"}
]

Examples:

[
  {"key": "application.listClient.[].firstName", "value": "Maria"}
]

Multiple Array Conditions:

[
  {"key": "application.listClient.[].firstName", "value": "Maria"},
  {"key": "application.listClient.[].department", "value": "Engineering"}
]

Nested Array Example:

[
  {"key": "application.clients.[].addresses.[].city", "value": "New York"}
]

Recommended for array searches over searchKeys
Returns processes where any array element matches the criteria
Supports deeply nested array structures

Cannot be used together with searchKey or searchKeys. Choose one approach.

Filtering parameters

processDefinitionNames (Array)

Purpose: Limit search to specific process definitions.Default: Searches all processes if omitted.Usage: Improves performance by narrowing search scope.Examples:

["client_onboarding"] - search only in client processes
["employee_registration", "contractor_onboarding"] - search in multiple process types

Best Practice: Always specify to improve search performance.

applicationIds (Array)

Purpose: Search across specific applications.Default: Searches current application if omitted.Usage: Enable cross-application searches.Examples:

["8dd20844-2dc5-4445-83a5-bbbcc82bed5f"] - search in specific app
["app-1-uuid", "app-2-uuid", "app-3-uuid"] - search across multiple apps

Use Case: Useful for consolidated reporting across different business units.

states (Array)

Purpose: Filter results by process instance status.Default: Returns all states if omitted.Available States:

"CREATED" - Process instance created but not started
"STARTED" - Process is currently running
"FINISHED" - Process completed successfully
"FAILED" - Process encountered an error and stopped
"TERMINATED" - Process was manually stopped/cancelled
"ONHOLD" - Process is paused or waiting for external input

Examples:

["FINISHED"] - only completed processes
["STARTED", "ONHOLD"] - active or paused processes
["FAILED", "TERMINATED"] - processes that didn’t complete normally

Best Practice: Use ["FINISHED"] for most business searches to get complete data.

Date range parameters

processStartDateAfter (String)

Purpose: Include only processes started after the specified date.Format: ISO 8601 timestamp (YYYY-MM-DDTHH:MM:SSZ)Examples:

"2024-01-01T00:00:00Z" - processes started after Jan 1, 2024
"2024-06-15T09:30:00Z" - processes started after June 15, 2024 at 9:30 AM

Use Cases:

Monthly reports: "2024-03-01T00:00:00Z"
Recent activity: "2024-05-20T00:00:00Z"

processStartDateBefore (String)

Purpose: Include only processes started before the specified date.Format: ISO 8601 timestamp (YYYY-MM-DDTHH:MM:SSZ)Examples:

"2024-12-31T23:59:59Z" - processes started before end of 2024
"2024-06-30T23:59:59Z" - processes started before end of June 2024

Use Cases:

Historical analysis: "2024-01-01T00:00:00Z"
Quarterly reports: "2024-03-31T23:59:59Z"

Process states explained

Understanding process states is crucial for effective searching:

State	Description	When to Use
CREATED	Process instance exists but hasn’t started execution	Rarely used for business searches
STARTED	Process is actively running	Find ongoing processes, current workload
FINISHED	Process completed successfully	Most common for business data searches
FAILED	Process encountered an error	Error analysis, troubleshooting
TERMINATED	Process was manually cancelled	Audit trails, cancelled applications
ONHOLD	Process is paused/waiting	Active cases needing attention

Recommendation: Use ["FINISHED"] for most business searches to ensure you’re getting complete, reliable data.

Response structure

The search returns a JSON object with these fields:

result (Array): List of matching process instances
- processInstanceUUID: Unique process identifier
- state: Current process state
- processStartDate: When the process started
- data: The actual process data with your searchable fields
tooManyResults (Boolean): True if more than 50 results found (limit applied)
success (Boolean): Whether the search completed successfully
errorMessage (String): Error details if search failed

Use cases & examples

HR employee lookup

Search for employees by department, position, or location across HR systems.

Compliance Auditing

Locate specific transactions or approvals for regulatory compliance.

Business Intelligence

Analyze process data patterns and generate reports.

Multi-contact Search

Find processes containing specific individuals within contact lists or arrays.

Real-world example: Employee directory search

HR needs to find employees

HR department needs to find all employees named “Maria” across different departments.

Search within employee arrays

{
  "searchByPaths": [
    {"key": "application.employees.[].firstName", "value": "Maria"}
  ],
  "processDefinitionNames": ["employee_onboarding"],
  "states": ["FINISHED"]
}

Get comprehensive results

Receive all employee records containing anyone named “Maria” in their employee lists.

Refine search if needed

Add additional criteria like department or role to narrow results further.

Real-world example: Customer support search

Customer calls with issue

Support agent needs to find customer’s account quickly.

Search by multiple criteria

{
  "searchKeys": [
    {"key": "customer.email", "value": "customer@email.com"},
    {"key": "customer.status", "value": "Active"}
  ],
  "processDefinitionNames": ["customer_onboarding"],
  "states": ["FINISHED"]
}

Get comprehensive results

Receive full customer profile with account details, order history, and support tickets.

Resolve customer issue

Use the retrieved data to address the customer’s concern effectively.

Best practices

Array Search Optimization

Use searchByPaths for array searches instead of searchKeys when possible
Index array fields properly using [] notation in Process Settings
Consider array size - large arrays may impact search performance
Combine array searches with filters to reduce result sets
Test nested array searches thoroughly before deploying to production

Performance optimization

Limit search scope using processDefinitionNames and states
Use date ranges for time-sensitive searches
Index only frequently searched fields to reduce storage overhead
Monitor search response times and optimize queries
Avoid wildcard searches on large datasets

Data Modeling

Use consistent field naming across processes
Normalize data formats (dates, phone numbers, etc.)
Consider search patterns when designing data structures
Document indexed fields for team reference
Plan for data growth and scaling needs
Design arrays with search in mind - consider what fields will be searched

Security & Compliance

Don’t index sensitive data (SSN, passwords, etc.)
Implement proper access controls for search endpoints
Log search activities for audit trails
Sanitize search inputs to prevent injection attacks
Follow data retention policies for search results

Error Handling

Handle empty results gracefully in your UI
Implement retry logic for failed searches
Provide meaningful error messages to users
Set reasonable timeouts for search operations
Monitor and alert on search failures

Troubleshooting

No Search Results

Possible Causes:

Fields not indexed in Process Settings → Data Search
Incorrect field path (case-sensitive)
Process instances not in expected state
Elasticsearch indexing delay
Array notation missing for array searches

Solutions:

Verify field indexing configuration
Check exact field paths in process data
Ensure processes are in FINISHED state
Wait 30-60 seconds after process completion
Use [] notation for array field searches

Array Search Issues

Possible Causes:

Missing [] notation in field path
Array fields not properly indexed
Using wrong parameter (searchKeys vs searchByPaths)
Array is empty or doesn’t contain expected data

Solutions:

Ensure field path includes [] for arrays (e.g., list.[].field)
Verify array fields are indexed with [] notation
Use searchByPaths for array searches when possible
Check that arrays contain the expected data structure
Test with simple array structures first

Search Timeout Errors

Possible Causes:

Query too broad (searching all data)
Elasticsearch cluster performance issues
Large dataset without proper filtering
Complex nested array searches

Solutions:

Add more specific filters (processDefinitionNames, states)
Use date ranges to limit scope
Check Elasticsearch cluster health
Optimize indexing strategy
Simplify complex array search patterns

Invalid Search Key Errors

Possible Causes:

Typos in field paths
Field doesn’t exist in process data
Incorrect JSON format in request
Missing array notation for array fields

Solutions:

Verify field paths exist in process instances
Check for case sensitivity
Validate JSON syntax
Test with simple field paths first
Add [] notation for array field searches

Connection Issues

Possible Causes:

Kafka topics not properly configured
Network connectivity problems
Service authentication issues

Solutions:

Verify Kafka topic configuration
Check network connectivity
Validate service credentials
Review FlowX Engine logs

Configuration templates

{
  "searchKey": "your.field.path",
  "value": "search_value",
  "processDefinitionNames": ["your_process"],
  "states": ["FINISHED"]
}

Elasticsearch setup

Configure Elasticsearch indexing for FlowX Engine

Data search service setup

Complete deployment and configuration guide

Process designer

Learn how to design processes with searchable data

Getting Started

Platform overview

FlowX.AI Designer

Building projects

Building blocks

Platform deep dive

API reference

FlowX.AI Data Search

Simple Search

Advanced Search

Array Search

Cross-Application

Date Filtering

Prerequisites

Quick start

1. Set up your data process

Configure Field Indexing

2. Create your search process

3. Configure search parameters

4. Handle search results

Search parameters reference

Quick reference table

Basic search parameters

Filtering parameters

Date range parameters

Process states explained

Response structure

Use cases & examples

HR employee lookup

Compliance Auditing

Business Intelligence

Multi-contact Search

Real-world example: Employee directory search

Real-world example: Customer support search

Best practices

Troubleshooting

Configuration templates

Elasticsearch setup

Data search service setup

Process designer

Getting Started

Platform overview

FlowX.AI Designer

Building projects

Building blocks

Platform deep dive

API reference

Simple Search

Advanced Search

Array Search

Cross-Application

Date Filtering

​Prerequisites

​Quick start

​1. Set up your data process

​Configure Field Indexing

​2. Create your search process

​3. Configure search parameters

​4. Handle search results

​Search parameters reference

​Quick reference table

​Basic search parameters

​Filtering parameters

​Date range parameters

​Process states explained

​Response structure

​Use cases & examples

HR employee lookup

Compliance Auditing

Business Intelligence

Multi-contact Search

​Real-world example: Employee directory search

​Real-world example: Customer support search

​Best practices

​Troubleshooting

​Configuration templates

​Related resources

Elasticsearch setup

Data search service setup

Process designer

Prerequisites

Quick start

1. Set up your data process

Configure Field Indexing

2. Create your search process

3. Configure search parameters

4. Handle search results

Search parameters reference

Quick reference table

Basic search parameters

Filtering parameters

Date range parameters

Process states explained

Response structure

Use cases & examples

Real-world example: Employee directory search

Real-world example: Customer support search

Best practices

Troubleshooting

Configuration templates

Related resources