LogoLogo
Knowledge BaseCommunityRelease NotesRequest Demo
  • Overview
  • Quick Start
    • Onboarding Guide
  • Data Sources & Transports
    • Supported Logs
      • 1Password Logs
      • Apache Logs
      • AppOmni Logs
      • Asana Logs
      • Atlassian Logs
      • Auditd Logs
      • Auth0 Logs
      • AWS Logs
        • AWS ALB
        • AWS Aurora
        • AWS CloudFront
        • AWS CloudTrail
        • AWS CloudWatch
        • AWS Config
        • AWS EKS
        • AWS GuardDuty
        • AWS Security Hub
        • Amazon Security Lake
        • AWS S3
        • AWS Transit Gateway
        • AWS VPC
        • AWS WAF
      • Azure Monitor Logs
      • Bitwarden Logs
      • Box Logs
      • Carbon Black Logs
      • Cisco Umbrella Logs
      • Cloudflare Logs
      • CrowdStrike Logs
        • CrowdStrike Falcon Data Replicator
        • CrowdStrike Event Streams
      • Docker Logs
      • Dropbox Logs
      • Duo Security Logs
      • Envoy Logs
      • Fastly Logs
      • Fluentd Logs
      • GCP Logs
      • GitHub Logs
      • GitLab Logs
      • Google Workspace Logs
      • Heroku Logs
      • Jamf Pro Logs
      • Juniper Logs
      • Lacework Logs
        • Lacework Alert Channel Webhook
        • Lacework Export
      • Material Security Logs
      • Microsoft 365 Logs
      • Microsoft Entra ID Audit Logs
      • Microsoft Graph Logs
      • MongoDB Atlas Logs
      • Netskope Logs
      • Nginx Logs
      • Notion Logs
      • Okta Logs
      • OneLogin Logs
      • Orca Security Logs (Beta)
      • Osquery Logs
      • OSSEC Logs
      • Proofpoint Logs
      • Push Security Logs
      • Rapid7 Logs
      • Salesforce Logs
      • SentinelOne Logs
      • Slack Logs
      • Snowflake Audit Logs (Beta)
      • Snyk Logs
      • Sophos Logs
      • Sublime Security Logs
      • Suricata Logs
      • Sysdig Logs
      • Syslog Logs
      • Tailscale Logs
      • Teleport Logs
      • Tenable Vulnerability Management Logs
      • Thinkst Canary Logs
      • Tines Logs
      • Tracebit Logs
      • Windows Event Logs
      • Wiz Logs
      • Zeek Logs
      • Zendesk Logs
      • Zoom Logs
      • Zscaler Logs
        • Zscaler ZIA
        • Zscaler ZPA
    • Custom Logs
      • Log Schema Reference
      • Transformations
      • Script Log Parser (Beta)
      • Fastmatch Log Parser
      • Regex Log Parser
      • CSV Log Parser
    • Data Transports
      • HTTP Source
      • AWS Sources
        • S3 Source
        • CloudWatch Logs Source
        • SQS Source
          • SNS Source
        • EventBridge
      • Google Cloud Sources
        • Cloud Storage (GCS) Source
        • Pub/Sub Source
      • Azure Blob Storage Source
    • Monitoring Log Sources
    • Ingestion Filters
      • Raw Event Filters
      • Normalized Event Filters (Beta)
    • Data Pipeline Tools
      • Chronosphere Onboarding Guide
      • Cribl Onboarding Guide
      • Fluent Bit Onboarding Guide
        • Fluent Bit Configuration Examples
      • Fluentd Onboarding Guide
        • General log forwarding via Fluentd
        • MacOS System Logs to S3 via Fluentd
        • Syslog to S3 via Fluentd
        • Windows Event Logs to S3 via Fluentd (Legacy)
        • GCP Audit to S3 via Fluentd
      • Observo Onboarding Guide
      • Tarsal Onboarding Guide
    • Tech Partner Log Source Integrations
  • Detections
    • Using Panther-managed Detections
      • Detection Packs
    • Rules and Scheduled Rules
      • Writing Python Detections
        • Python Rule Caching
        • Data Models
        • Global Helper Functions
      • Modifying Detections with Inline Filters (Beta)
      • Derived Detections (Beta)
        • Using Derived Detections to Avoid Merge Conflicts
      • Using the Simple Detection Builder
      • Writing Simple Detections
        • Simple Detection Match Expression Reference
        • Simple Detection Error Codes
    • Correlation Rules (Beta)
      • Correlation Rule Reference
    • PyPanther Detections (Beta)
      • Creating PyPanther Detections
      • Registering, Testing, and Uploading PyPanther Detections
      • Managing PyPanther Detections in the Panther Console
      • PyPanther Detections Style Guide
      • pypanther Library Reference
      • Using the pypanther Command Line Tool
    • Signals
    • Policies
    • Testing
      • Data Replay (Beta)
    • Framework Mapping and MITRE ATT&CK® Matrix
  • Cloud Security Scanning
    • Cloud Resource Attributes
      • AWS
        • ACM Certificate
        • CloudFormation Stack
        • CloudWatch Log Group
        • CloudTrail
        • CloudTrail Meta
        • Config Recorder
        • Config Recorder Meta
        • DynamoDB Table
        • EC2 AMI
        • EC2 Instance
        • EC2 Network ACL
        • EC2 SecurityGroup
        • EC2 Volume
        • EC2 VPC
        • ECS Cluster
        • EKS Cluster
        • ELBV2 Application Load Balancer
        • GuardDuty Detector
        • GuardDuty Detector Meta
        • IAM Group
        • IAM Policy
        • IAM Role
        • IAM Root User
        • IAM User
        • KMS Key
        • Lambda Function
        • Password Policy
        • RDS Instance
        • Redshift Cluster
        • Route 53 Domains
        • Route 53 Hosted Zone
        • S3 Bucket
        • WAF Web ACL
  • Alerts & Destinations
    • Alert Destinations
      • Amazon SNS Destination
      • Amazon SQS Destination
      • Asana Destination
      • Blink Ops Destination
      • Custom Webhook Destination
      • Discord Destination
      • GitHub Destination
      • Google Pub/Sub Destination (Beta)
      • Incident.io Destination
      • Jira Cloud Destination
      • Jira Data Center Destination (Beta)
      • Microsoft Teams Destination
      • Mindflow Destination
      • OpsGenie Destination
      • PagerDuty Destination
      • Rapid7 Destination
      • ServiceNow Destination (Custom Webhook)
      • Slack Bot Destination
      • Slack Destination (Webhook)
      • Splunk Destination (Beta)
      • Tines Destination
      • Torq Destination
    • Assigning and Managing Alerts
      • Managing Alerts in Slack
    • Alert Runbooks
      • Panther-managed Policies Runbooks
        • AWS CloudTrail Is Enabled In All Regions
        • AWS CloudTrail Sending To CloudWatch Logs
        • AWS KMS CMK Key Rotation Is Enabled
        • AWS Application Load Balancer Has Web ACL
        • AWS Access Keys Are Used Every 90 Days
        • AWS Access Keys are Rotated Every 90 Days
        • AWS ACM Certificate Is Not Expired
        • AWS Access Keys not Created During Account Creation
        • AWS CloudTrail Has Log Validation Enabled
        • AWS CloudTrail S3 Bucket Has Access Logging Enabled
        • AWS CloudTrail Logs S3 Bucket Not Publicly Accessible
        • AWS Config Is Enabled for Global Resources
        • AWS DynamoDB Table Has Autoscaling Targets Configured
        • AWS DynamoDB Table Has Autoscaling Enabled
        • AWS DynamoDB Table Has Encryption Enabled
        • AWS EC2 AMI Launched on Approved Host
        • AWS EC2 AMI Launched on Approved Instance Type
        • AWS EC2 AMI Launched With Approved Tenancy
        • AWS EC2 Instance Has Detailed Monitoring Enabled
        • AWS EC2 Instance Is EBS Optimized
        • AWS EC2 Instance Running on Approved AMI
        • AWS EC2 Instance Running on Approved Instance Type
        • AWS EC2 Instance Running in Approved VPC
        • AWS EC2 Instance Running On Approved Host
        • AWS EC2 Instance Running With Approved Tenancy
        • AWS EC2 Instance Volumes Are Encrypted
        • AWS EC2 Volume Is Encrypted
        • AWS GuardDuty is Logging to a Master Account
        • AWS GuardDuty Is Enabled
        • AWS IAM Group Has Users
        • AWS IAM Policy Blocklist Is Respected
        • AWS IAM Policy Does Not Grant Full Administrative Privileges
        • AWS IAM Policy Is Not Assigned Directly To User
        • AWS IAM Policy Role Mapping Is Respected
        • AWS IAM User Has MFA Enabled
        • AWS IAM Password Used Every 90 Days
        • AWS Password Policy Enforces Complexity Guidelines
        • AWS Password Policy Enforces Password Age Limit Of 90 Days Or Less
        • AWS Password Policy Prevents Password Reuse
        • AWS RDS Instance Is Not Publicly Accessible
        • AWS RDS Instance Snapshots Are Not Publicly Accessible
        • AWS RDS Instance Has Storage Encrypted
        • AWS RDS Instance Has Backups Enabled
        • AWS RDS Instance Has High Availability Configured
        • AWS Redshift Cluster Allows Version Upgrades
        • AWS Redshift Cluster Has Encryption Enabled
        • AWS Redshift Cluster Has Logging Enabled
        • AWS Redshift Cluster Has Correct Preferred Maintenance Window
        • AWS Redshift Cluster Has Sufficient Snapshot Retention Period
        • AWS Resource Has Minimum Number of Tags
        • AWS Resource Has Required Tags
        • AWS Root Account Has MFA Enabled
        • AWS Root Account Does Not Have Access Keys
        • AWS S3 Bucket Name Has No Periods
        • AWS S3 Bucket Not Publicly Readable
        • AWS S3 Bucket Not Publicly Writeable
        • AWS S3 Bucket Policy Does Not Use Allow With Not Principal
        • AWS S3 Bucket Policy Enforces Secure Access
        • AWS S3 Bucket Policy Restricts Allowed Actions
        • AWS S3 Bucket Policy Restricts Principal
        • AWS S3 Bucket Has Versioning Enabled
        • AWS S3 Bucket Has Encryption Enabled
        • AWS S3 Bucket Lifecycle Configuration Expires Data
        • AWS S3 Bucket Has Logging Enabled
        • AWS S3 Bucket Has MFA Delete Enabled
        • AWS S3 Bucket Has Public Access Block Enabled
        • AWS Security Group Restricts Ingress On Administrative Ports
        • AWS VPC Default Security Group Restricts All Traffic
        • AWS VPC Flow Logging Enabled
        • AWS WAF Has Correct Rule Ordering
        • AWS CloudTrail Logs Encrypted Using KMS CMK
      • Panther-managed Rules Runbooks
        • AWS CloudTrail Modified
        • AWS Config Service Modified
        • AWS Console Login Failed
        • AWS Console Login Without MFA
        • AWS EC2 Gateway Modified
        • AWS EC2 Network ACL Modified
        • AWS EC2 Route Table Modified
        • AWS EC2 SecurityGroup Modified
        • AWS EC2 VPC Modified
        • AWS IAM Policy Modified
        • AWS KMS CMK Loss
        • AWS Root Activity
        • AWS S3 Bucket Policy Modified
        • AWS Unauthorized API Call
    • Tech Partner Alert Destination Integrations
  • Investigations & Search
    • Search
      • Search Filter Operators
    • Data Explorer
      • Data Explorer SQL Search Examples
        • CloudTrail logs queries
        • GitHub Audit logs queries
        • GuardDuty logs queries
        • Nginx and ALB Access logs queries
        • Okta logs queries
        • S3 Access logs queries
        • VPC logs queries
    • Visualization and Dashboards
      • Custom Dashboards (Beta)
      • Panther-Managed Dashboards
    • Standard Fields
    • Saved and Scheduled Searches
      • Templated Searches
        • Behavioral Analytics and Anomaly Detection Template Macros (Beta)
      • Scheduled Search Examples
    • Search History
    • Data Lakes
      • Snowflake
        • Snowflake Configuration for Optimal Search Performance
      • Athena
  • PantherFlow (Beta)
    • PantherFlow Quick Reference
    • PantherFlow Statements
    • PantherFlow Operators
      • Datatable Operator
      • Extend Operator
      • Join Operator
      • Limit Operator
      • Project Operator
      • Range Operator
      • Sort Operator
      • Search Operator
      • Summarize Operator
      • Union Operator
      • Visualize Operator
      • Where Operator
    • PantherFlow Data Types
    • PantherFlow Expressions
    • PantherFlow Functions
      • Aggregation Functions
      • Date/time Functions
      • String Functions
      • Array Functions
      • Math Functions
      • Control Flow Functions
      • Regular Expression Functions
      • Snowflake Functions
      • Data Type Functions
      • Other Functions
    • PantherFlow Example Queries
      • PantherFlow Examples: Threat Hunting Scenarios
      • PantherFlow Examples: SOC Operations
      • PantherFlow Examples: Panther Audit Logs
  • Enrichment
    • Custom Lookup Tables
      • Creating a GreyNoise Lookup Table
      • Lookup Table Examples
        • Using Lookup Tables: 1Password UUIDs
      • Lookup Table Specification Reference
    • Identity Provider Profiles
      • Okta Profiles
      • Google Workspace Profiles
    • Anomali ThreatStream
    • IPinfo
    • Tor Exit Nodes
    • TrailDiscover (Beta)
  • Panther AI (Beta)
  • System Configuration
    • Role-Based Access Control
    • Identity & Access Integrations
      • Azure Active Directory SSO
      • Duo SSO
      • G Suite SSO
      • Okta SSO
        • Okta SCIM
      • OneLogin SSO
      • Generic SSO
    • Panther Audit Logs
      • Querying and Writing Detections for Panther Audit Logs
      • Panther Audit Log Actions
    • Notifications and Errors (Beta)
      • System Errors
    • Panther Deployment Types
      • SaaS
      • Cloud Connected
        • Configuring Snowflake for Cloud Connected
        • Configuring AWS for Cloud Connected
        • Pre-Deployment Tools
      • Legacy Configurations
        • Snowflake Connected (Legacy)
        • Customer-configured Snowflake Integration (Legacy)
        • Self-Hosted Deployments (Legacy)
          • Runtime Environment
  • Panther Developer Workflows
    • Panther Developer Workflows Overview
    • Using panther-analysis
      • Public Fork
      • Private Clone
      • Panther Analysis Tool
        • Install, Configure, and Authenticate with the Panther Analysis Tool
        • Panther Analysis Tool Commands
        • Managing Lookup Tables and Enrichment Providers with the Panther Analysis Tool
      • CI/CD for Panther Content
        • Deployment Workflows Using Panther Analysis Tool
          • Managing Panther Content via CircleCI
          • Managing Panther Content via GitHub Actions
        • Migrating to a CI/CD Workflow
    • Panther API
      • REST API (Beta)
        • Alerts
        • Alert Comments
        • API Tokens
        • Data Models
        • Globals
        • Log Sources
        • Queries
        • Roles
        • Rules
        • Scheduled Rules
        • Simple Rules
        • Policies
        • Users
      • GraphQL API
        • Alerts & Errors
        • Cloud Account Management
        • Data Lake Queries
        • Log Source Management
        • Metrics
        • Schemas
        • Token Rotation
        • User & Role Management
      • API Playground
    • Terraform
      • Managing AWS S3 Log Sources with Terraform
      • Managing HTTP Log Sources with Terraform
    • pantherlog Tool
    • Converting Sigma Rules
  • Resources
    • Help
      • Operations
      • Security and Privacy
        • Security Without AWS External ID
      • Glossary
      • Legal
    • Panther System Architecture
Powered by GitBook
On this page
  • Overview
  • Common Data Lake query operations
  • End-to-end examples

Was this helpful?

  1. Panther Developer Workflows
  2. Panther API
  3. GraphQL API

Data Lake Queries

Panther API search operations

PreviousCloud Account ManagementNextLog Source Management

Last updated 1 month ago

Was this helpful?

Overview

The Panther API supports the following data lake operations:

  • Listing your data lake databases, tables, and columns

  • Executing a data lake (Data Explorer) query using SQL

  • Executing a Search query

  • Canceling any currently-running query

  • Fetching the details of any previously executed query

  • Listing all currently running or previously-executed queries with optional filters

You can invoke Panther's API by using your Console's API Playground, or the GraphQL-over-HTTP API. Learn more about these methods on .

See the sections below for GraphQL queries, mutations, and end-to-end workflow examples around core data lake query operations.

Queries managed via the API must be written in SQL; they cannot use .

Common Data Lake query operations

Below are some of the most common GraphQL Data Lake query operations in Panther. These examples demonstrate the documents you have to send using a GraphQL client (or curl) to make a call to Panther's GraphQL API.

Database Entities

# `AllDatabaseEntities` is a nickname for the operation
query AllDatabaseEntities {
  dataLakeDatabases {
     name
     description
     tables {
       name
       description
       columns {
         name
         description
         type
       }
     }
   }
 }
# `DatabaseEntities` is a nickname for the operation
query DatabaseEntities {
  dataLakeDatabase(name: "panther_logs.public") {
     name
     description
     tables {
       name
       description
       columns {
         name
         description
         type
       }
     }
  }
}

Executing queries

# `IssueDataLakeQuery` is a nickname for the operation
mutation IssueDataLakeQuery {
  executeDataLakeQuery(input: {
    sql: "select * from panther_logs.public.aws_alb limit 50"
  }) {
     id # the unique ID of the query
  }
}
# `IssueIndicatorSearchQuery` is a nickname for the operation
mutation IssueIndicatorSearchQuery {
  executeIndicatorSearchQuery(input: {
    indicators: ["286103014039", "126103014049"]
    startTime: "2022-04-01T00:00:00.000Z",
    endTime: "2022-04-30T23:59:59.000Z"
    indicatorName: p_any_aws_account_ids # or leave blank for auto-detect
  }) {
     id # the unique ID of the query
  }
}
# `AbandonQuery` is a nickname for the operation
mutation AbandonQuery {
  cancelDataLakeQuery(input: { id: "1234-5678" }) {
     id # return the ID that got canceled
  }
}

Fetching results for a data lake or Search query

When you execute a data lake or Search query, it can take a few seconds to a few minutes for results to come back. To confirm that the query has completed, you must check the status of the query (polling).

You can use the following query to check the query status, while also fetching its results if available:

# `QueryResults` is a nickname for the operation
query QueryResults {
  dataLakeQuery(id: "1234-1234-1234-1234") { # the unique ID of the query
    message
    status
    results {
      edges {
        node
      }
    }
  }
}
# `QueryResults` is a nickname for the operation
query QueryResults {
  dataLakeQuery(id: "1234-1234-1234-1234") { # the unique ID of the query
    message
    status
    results(input: { cursor: "5678-5678-5678-5678" }) { # the value of `endCursor`
      edges {
        node
      }
      pageInfo
        endCursor
        hasNextPage
      }
    }
  }

The expected values of status and results depend on the query's status:

  • If the query is still running:

    • status will have a value of running

    • results will have a value of null

  • If the query has failed:

    • status will have a value of failed

    • results will have a value of null and the error message will be available in the message key

  • If the query has completed

    • status will have a value of succeeded

    • results will be populated

Fetching metadata around a data lake or Search query

In the example above, we requested the results of a Panther query. It is also possible to request additional metadata around the query.

In the following example, we request these metadata along the first page of results:

# `QueryMetadata` is a nickname for the operation
query QueryMetadata {
  dataLakeQuery(id: "1234-1234-1234-1234") { # the unique ID of the query
    name
    isScheduled
    issuedBy {
      ... on User {
        email
      }
      ... on APIToken {
        name
      } 
    }
    sql
    message
    status
    startedAt
    completedAt
    results {
      edges {
        node
      }
    }
  }
}

Listing data lake and Search queries

# `ListDataLakeQueries` is a nickname for the operation
query ListDataLakeQueries {
  dataLakeQueries {
    name
    isScheduled
    issuedBy {
      ... on User {
        email
      }
      ... on APIToken {
        name
    } 
    }
    sql
    message
    status
    startedAt
    completedAt
    results { # we're only fetching the first page of results for each query
      edges {
        node
      }
    }
  }
# `ListDataLakeQueries` is a nickname for the operation
query ListDataLakeQueries {
  dataLakeQueries(input: { cursor: "5678-5678-5678-5678" }) { # the value of `endCursor`
    name
    isScheduled
    issuedBy {
      ... on User {
        email
      }
      ... on APIToken {
        name
    } 
    }
    sql
    message
    status
    startedAt
    completedAt
    results { # we're only fetching the first page of results for each query
      edges {
        node
      }
    }
    pageInfo {
      endCursor
      hasNextPage
    }
  }
# `ListDataLakeQueries` is a nickname for the operation
query ListDataLakeQueries {
  dataLakeQueries(input: { contains: "aws_alb", isScheduled: true }) {
    name
    isScheduled
    issuedBy {
      ... on User {
        email
      }
      ... on APIToken {
        name
    } 
    }
    sql
    message
    status
    startedAt
    completedAt
    results { # we're only fetching the first page of results for each query
      edges {
        node
      }
    }
  }

End-to-end examples

Execute a data lake (Data Explorer) Query

// npm install graphql graphql-request

import { GraphQLClient, gql } from 'graphql-request';

const client = new GraphQLClient(
  'YOUR_PANTHER_API_URL', 
  { headers: { 'X-API-Key': 'YOUR_API_KEY' } 
});

// `IssueQuery` is a nickname for the query. You can fully omit it.
const issueQuery = gql`
  mutation IssueQuery($sql: String!) {
    executeDataLakeQuery(input: { sql: $sql }) {
      id
    }
  }
`;

// `GetQueryResults` is a nickname for the query. You can fully omit it.
const getQueryResults = gql`
  query GetQueryResults($id: ID!, $cursor: String) {
    dataLakeQuery(id: $id) {
      message
      status
      results(input: { cursor: $cursor }) {
        edges {
          node
        }
        pageInfo {
          endCursor
          hasNextPage
        }
      }
    }
  }
`;

(async () => {
  try {
    // an accumulator that holds all result nodes that we fetch
    let allResults = [];
    // a helper to know when to exit the loop
    let hasMore = true;
    // the pagination cursor
    let cursor = null;

    // issue a query
    const mutationData = await client.request(issueQuery, {
      sql: 'select * from panther_logs.public.aws_alb limit 5',
    });

    // Start polling the query until it returns results. From there,
    // keep fetching pages until there are no more left
    do {
      const queryData = await client.request(getQueryResults, {
        id: mutationData.executeDataLakeQuery.id,
        cursor,
      });

      // if it's still running, print a message and keep polling
      if (queryData.dataLakeQuery.status === 'running') {
        console.log(queryData.dataLakeQuery.message);
        continue;
      }

      // if it's not running & it's not completed, then it's
      // either cancelled or it has errored out. In this case,
      // throw an exception
      if (queryData.dataLakeQuery.status !== 'succeeded') {
        throw new Error(queryData.dataLakeQuery.message);
      }

      allResults = [...allResults, ...queryData.dataLakeQuery.results.edges.map(edge => edge.node)];

      hasMore = queryData.dataLakeQuery.results.pageInfo.hasNextPage;
      cursor = queryData.dataLakeQuery.results.pageInfo.endCursor;
    } while (hasMore);

    console.log(`Your query returned ${allResults.length} result(s)!`);
  } catch (err) {
    console.error(err.response);
  }
})();
# pip install gql aiohttp

from gql import gql, Client
from gql.transport.aiohttp import AIOHTTPTransport

transport = AIOHTTPTransport(
  url="YOUR_PANTHER_API_URL",
  headers={"X-API-Key": "YOUR_API_KEY"}
)

client = Client(transport=transport, fetch_schema_from_transport=True)

# `IssueQuery` is a nickname for the query. You can fully omit it.
issue_query = gql(
    """
    mutation IssueQuery($sql: String!) {
        executeDataLakeQuery(input: { sql: $sql }) {
            id
        }
    }
    """
)

# `GetQueryResults` is a nickname for the query. You can fully omit it.
get_query_results = gql(
    """
    query GetQueryResults($id: ID!, $cursor: String) {
        dataLakeQuery(id: $id) {
            message
            status
            results(input: { cursor: $cursor }) {
                edges {
                    node
                }
                pageInfo {
                    endCursor
                    hasNextPage
                }
            }
        }
    }
    """
)

# an accumulator that holds all results that we fetch from all pages
all_results = []
# a helper to know when to exit the loop.
has_more = True
# the pagination cursor
cursor = None

# Issue a Data Lake (Data Explorer) query
mutation_data = client.execute(
    issue_query,
    variable_values={
        "sql": "select * from panther_logs.public.aws_alb limit 5"
    }
)

# Start polling the query until it returns results. From there,
# keep fetching pages until there are no more left
while has_more:
    query_data = client.execute(
        get_query_results,
        variable_values = {
            "id": mutation_data["executeDataLakeQuery"]["id"],
            "cursor": cursor
        }
    )
    
    # if it's still running, print a message and keep polling
    if query_data["dataLakeQuery"]["status"] == "running":
        print(query_data["dataLakeQuery"]["message"])
        continue
    
    # if it's not running & it's not completed, then it's
    # either cancelled or it has errored out. In this case,
    # throw an exception
    if query_data["dataLakeQuery"]["status"] != "succeeded":
        raise Exception(query_data["dataLakeQuery"]["message"])

    all_results.extend([edge["node"] for edge in query_data["dataLakeQuery"]["results"]["edges"]])
    has_more = query_data["dataLakeQuery"]["results"]["pageInfo"]["hasNextPage"]
    cursor = query_data["dataLakeQuery"]["results"]["pageInfo"]["endCursor"]

print(f'Query returned {len(all_results)} results(s)!')

Execute a Search query

// npm install graphql graphql-request

import { GraphQLClient, gql } from 'graphql-request';

const client = new GraphQLClient(
  'YOUR_PANTHER_API_URL', 
  { headers: { 'X-API-Key': 'YOUR_API_KEY' } 
});

// `IssueQuery` is a nickname for the query. You can fully omit it.
const issueQuery = gql`
  mutation IssueQuery($input: ExecuteIndicatorSearchQueryInput!) {
    executeIndicatorSearchQuery(input: $input) {
      id
    }
  }
`;

// `GetQueryResults` is a nickname for the query. You can fully omit it.
const getQueryResults = gql`
  query GetQueryResults($id: ID!, $cursor: String) {
    dataLakeQuery(id: $id) {
      message
      status
      results(input: { cursor: $cursor }) {
        edges {
          node
        }
        pageInfo {
          endCursor
          hasNextPage
        }
      }
    }
  }
`;

(async () => {
  try {
    // an accumulator that holds all result nodes that we fetch
    let allResults = [];
    // a helper to know when to exit the loop
    let hasMore = true;
    // the pagination cursor
    let cursor = null;

    // issue a query
    const mutationData = await client.request(issueQuery, {
      input: {         
        indicators: ["226103014039"],
        startTime: "2022-03-29T00:00:00.001Z",
        endTime: "2022-03-30T00:00:00.001Z",
        indicatorName: "p_any_aws_account_ids"
      }
    });

    // Keep fetching pages until there are no more left
    do {
      const queryData = await client.request(getQueryResults, {
        id: mutationData.executeIndicatorSearchQuery.id,
        cursor,
      });

      // if it's still running, print a message and keep polling
      if (queryData.dataLakeQuery.status === 'running') {
        console.log(queryData.dataLakeQuery.message);
        continue;
      }

      // if it's not running & it's not completed, then it's
      // either cancelled or it has errored out. In this case,
      // throw an exception
      if (queryData.dataLakeQuery.status !== 'succeeded') {
        throw new Error(queryData.dataLakeQuery.message);
      }

      allResults = [...allResults, ...queryData.dataLakeQuery.results.edges.map(edge => edge.node)];

      hasMore = queryData.dataLakeQuery.results.pageInfo.hasNextPage;
      cursor = queryData.dataLakeQuery.results.pageInfo.endCursor;
    } while (hasMore);

    console.log(`Your query returned ${allResults.length} result(s)!`);
  } catch (err) {
    console.error(err.response);
  }
})();
# pip install gql aiohttp

from gql import gql, Client
from gql.transport.aiohttp import AIOHTTPTransport

transport = AIOHTTPTransport(
  url="YOUR_PANTHER_API_URL",
  headers={"X-API-Key": "YOUR_API_KEY"}
)

client = Client(transport=transport, fetch_schema_from_transport=True)

# `IssueQuery` is a nickname for the query. You can fully omit it.
issue_query = gql(
    """
    mutation IssueQuery($input: ExecuteIndicatorSearchQueryInput!) {
        executeIndicatorSearchQuery(input: $input) {
            id
        }
    }
    """
)

# `GetQueryResults` is a nickname for the query. You can fully omit it.
get_query_results = gql(
    """
    query GetQueryResults($id: ID!, $cursor: String) {
        dataLakeQuery(id: $id) {
            message
            status
            results(input: { cursor: $cursor }) {
                edges {
                    node
                }
                pageInfo {
                    endCursor
                    hasNextPage
                }
            }
        }
    }
    """
)

# an accumulator that holds all results that we fetch from all pages
all_results = []
# a helper to know when to exit the loop
has_more = True
# the pagination cursor
cursor = None

# Issue an Indicator Search query
mutation_data = client.execute(
    issue_query,
    variable_values={
        "input": {         
            "indicators": ["226103014039"],
            "startTime": "2022-03-29T00:00:00.001Z",
            "endTime": "2022-03-30T00:00:00.001Z",
            "indicatorName": "p_any_aws_account_ids"
        }
    }
)

# Start polling the query until it returns results. From there,
# keep fetching pages until there are no more left
while has_more:    
    query_data = client.execute(
        get_query_results,
        variable_values = {
            "id": mutation_data["executeIndicatorSearchQuery"]["id"],
            "cursor": cursor
        }
    )
    
    # if it's still running, print a message and keep polling
    if query_data["dataLakeQuery"]["status"] == "running":
        print(query_data["dataLakeQuery"]["message"])
        continue
    
    # if it's not running & it's not completed, then it's
    # either cancelled or it has errored out. In this case,
    # throw an exception
    if query_data["dataLakeQuery"]["status"] != "succeeded":
        raise Exception(query_data["dataLakeQuery"]["message"])

    all_results.extend([edge["node"] for edge in query_data["dataLakeQuery"]["results"]["edges"]])
    has_more = query_data["dataLakeQuery"]["results"]["pageInfo"]["hasNextPage"]
    cursor = query_data["dataLakeQuery"]["results"]["pageInfo"]["endCursor"]

print(f'Query returned {len(all_results)} results(s)!')

All of the above (along with the possible values for status) , along with additional fields you are allowed to request. Learn about the .

Below, we will build on the examples to showcase an end-to-end flow.

Common Operations
PantherFlow
Panther API
different ways to explore the Panther API schema here