Querying Recording Annotations and Bookmarks using the Analytics API

How-To-Articles
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Querying Recording Annotations and Bookmarks using the Analytics API

What This Guide Covers

This guide details the architectural approach and exact API payloads required to query recording annotations and bookmarks at scale using the Genesys Cloud Analytics API. You will build a production-ready query pipeline that correctly structures filter expressions, handles pagination boundaries, and extracts annotation metadata without triggering database throttling or index fragmentation.

Prerequisites, Roles & Licensing

  • Licensing Tier: CX 2 or CX 3 base subscription. Requires the Speech Analytics add-on or WEM Recording Analytics entitlement.
  • Granular Permissions: Analytics:Record:View, Recording:View, Recording:Annotation:View
  • OAuth Scopes: analytics:read, recording:view
  • External Dependencies: None. The recording indexing pipeline must be fully operational. Annotations must have passed the Speech Analytics or WEM processing stage and been committed to the analytics index.

The Implementation Deep-Dive

1. Architectural Distinction: Analytics Index vs. Object Store

Before constructing any query, you must understand where Genesys Cloud stores annotation data. The platform maintains two separate storage layers. The Recording Management API serves raw annotation objects from the document store. The Analytics API serves a flattened, time-indexed projection optimized for aggregation and filtering. When you query annotations through the Analytics API, you are not retrieving the full annotation payload. You are retrieving recording-level records that contain annotation metadata fields such as annotationType, annotationValue, annotationId, and annotationTimestamp.

We use the Analytics API for bulk retrieval, compliance auditing, and reporting. We use the Recording Management API when you need the exact JSON structure of a single annotation or need to programmatically create/update annotations. Mixing these two approaches causes unnecessary API call inflation and violates the platform’s data access patterns.

The Trap: Attempting to reconstruct full annotation objects by calling the Analytics API and then performing a secondary GET call per recording ID. This pattern creates a fan-out explosion. A single query returning 500 recordings triggers 500 synchronous HTTP requests to the Recording Management service. Under load, this exhausts your OAuth token’s rate limit and triggers 429 Too Many Requests responses from the ingress gateway.

Architectural Reasoning: The Analytics API is built on a columnar store with pre-computed dimensions. It answers questions like “how many recordings contain a dispute bookmark in the last 30 days” efficiently. It does not answer “what is the exact JSON payload of bookmark ID abc-123”. Design your integration to accept the flattened metadata from the Analytics API. If downstream systems require the full object, batch the recording IDs and process them asynchronously through a worker queue. Never fan out synchronously in a request-response cycle.

2. Constructing the Base Query Payload

The Analytics API accepts a POST request to /api/v2/analytics/records/query. The payload structure dictates how the indexing engine partitions the query. You must define filterBy, groupBy, timeRange, query, and pagination parameters. The filterBy array tells the engine which dimensions to materialize in the response. The query string uses a Lucene-inspired syntax for field-level filtering.

Use the following payload structure as your baseline. This configuration retrieves recordings containing any annotation, grouped by annotation type, within a specific time window.

POST /api/v2/analytics/records/query
Content-Type: application/json
Authorization: Bearer <access_token>

{
  "filterBy": ["recordings", "annotations"],
  "groupBy": ["annotationType", "annotationValue"],
  "timeRange": {
    "from": "2024-01-01T00:00:00Z",
    "to": "2024-01-31T23:59:59Z"
  },
  "query": "annotationType:'bookmark' OR annotationType:'tag'",
  "pageSize": 100,
  "page": 1,
  "sort": [
    {
      "field": "timestamp",
      "order": "desc"
    }
  ]
}

The filterBy array must include recordings to return recording-level records. Adding annotations ensures the engine joins the annotation dimension to the recording dimension. The groupBy array determines how the response aggregates data. If you omit groupBy, the API returns individual recording records with embedded annotation metadata. If you include groupBy, the API returns aggregated counts and distinct values. Choose based on your downstream consumer. Reporting dashboards require groupBy. Compliance export pipelines require flat records.

The Trap: Omitting the timeRange parameter or using an unbounded window. The Analytics engine enforces a hard limit on index scan range. If you submit a query without timeRange, the API defaults to the last 24 hours. If you explicitly set a range larger than 90 days, the engine throws a 400 Bad Request with a timeRangeTooLarge error. Many developers attempt to bypass this by chaining overlapping queries without respecting the index partition boundaries, which causes duplicate record retrieval and skewed aggregation counts.

Architectural Reasoning: The analytics index is partitioned by time. Queries are routed to specific partition shards based on the from and to values. Bounded time ranges allow the query planner to skip irrelevant shards entirely. Always constrain your timeRange to the minimum necessary window. If you require historical data beyond 90 days, implement a sliding window iterator that processes 30-day chunks sequentially. This aligns with the platform’s shard lifecycle and prevents memory exhaustion on the query coordinator nodes.

3. Filtering by Annotation Type and Value Precision

Annotations and bookmarks share the same underlying dimension but require distinct filter syntax. Bookmarks are typically stored with annotationType:'bookmark'. Tags, compliance flags, and speech analytics insights use annotationType:'tag' or annotationType:'insight'. The annotationValue field contains the human-readable label or the system-generated identifier.

To isolate specific bookmarks, you must use exact match syntax or wildcard patterns. The Analytics API does not support full-text search on annotationValue. It supports prefix matching and exact token matching.

POST /api/v2/analytics/records/query
Content-Type: application/json
Authorization: Bearer <access_token>

{
  "filterBy": ["recordings", "annotations"],
  "timeRange": {
    "from": "2024-03-01T00:00:00Z",
    "to": "2024-03-31T23:59:59Z"
  },
  "query": "annotationType:'bookmark' AND annotationValue:'dispute_escalation'",
  "pageSize": 100,
  "page": 1
}

When querying for multiple values, use the IN operator or parenthesized OR chains. The engine optimizes IN clauses by converting them to bitmap index intersections.

{
  "query": "annotationType:'bookmark' AND annotationValue IN ('dispute_escalation', 'refund_approved', 'manager_override')"
}

The Trap: Using case-insensitive assumptions on annotationValue. The analytics index preserves the exact casing of the annotation value at ingestion time. A query for annotationValue:'Dispute' will not match annotationValue:'dispute'. Many integrations fail because the UI displays normalized labels while the index stores the raw payload value. This mismatch results in zero-record responses that appear to be silent failures.

Architectural Reasoning: The indexing pipeline writes values exactly as they arrive from the Speech Analytics or WEM services. The platform does not apply automatic lowercasing or stemming to annotation values to preserve audit fidelity. You must normalize your query strings to match the ingestion standard. If your organization uses multiple annotation naming conventions, implement a pre-query normalization layer that maps UI labels to their indexed counterparts before sending the request. This prevents runtime filter mismatches and reduces query retry loops.

4. Pagination, Cursor Navigation, and State Management

The Analytics API uses offset-based pagination combined with a nextPage token. The response includes page, pageSize, count, items, and nextPage. When nextPage is null, the dataset is exhausted. When nextPage contains a value, you must pass it to the subsequent request instead of incrementing the page integer manually.

POST /api/v2/analytics/records/query
Content-Type: application/json
Authorization: Bearer <access_token>

{
  "filterBy": ["recordings", "annotations"],
  "timeRange": {
    "from": "2024-02-01T00:00:00Z",
    "to": "2024-02-28T23:59:59Z"
  },
  "query": "annotationType:'bookmark'",
  "pageSize": 100,
  "page": 1,
  "nextPage": "eyJwYWdlIjoyLCJza3lwIjoxMDB9"
}

The nextPage token encodes the internal cursor position and skip offset. It is opaque and platform-managed. You must store it exactly as returned. Do not parse it. Do not modify it. Do not cache it across different timeRange values.

The Trap: Reusing a nextPage token with a modified timeRange or pageSize. The cursor is bound to the exact query parameters used to generate it. Changing the timeRange invalidates the skip offset. The API returns a 400 Bad Request with invalidNextPageToken. Developers often attempt to optimize by reusing tokens across similar queries, which breaks the cursor state and causes the engine to re-scan the index from the beginning, multiplying compute costs.

Architectural Reasoning: The cursor token contains a serialized state object that includes the original query hash, partition shard pointers, and the last processed document ID. The query validator computes a hash of the incoming payload and compares it to the embedded hash in the nextPage token. A mismatch triggers immediate rejection. Implement a strict state machine that treats each query execution as an isolated transaction. Store the nextPage token in a transient cache keyed to the exact serialized query string. Evict the cache when any parameter changes. This guarantees cursor validity and prevents index re-scans.

5. Aggregation Strategies for High-Volume Environments

When processing millions of annotated recordings, retrieving flat records is inefficient. Use the groupBy parameter to shift computation to the analytics engine. The engine performs distributed aggregations across partition shards and returns a consolidated result set. This reduces network payload size and eliminates client-side deduplication logic.

POST /api/v2/analytics/records/query
Content-Type: application/json
Authorization: Bearer <access_token>

{
  "filterBy": ["recordings", "annotations"],
  "groupBy": ["annotationType", "annotationValue", "queueId"],
  "timeRange": {
    "from": "2024-01-01T00:00:00Z",
    "to": "2024-01-31T23:59:59Z"
  },
  "query": "annotationType:'bookmark'",
  "pageSize": 50
}

The response returns aggregated counts per dimension combination. You can add count metrics to the groupBy array to retrieve volume metrics directly. This pattern is mandatory for compliance reporting and capacity planning. It prevents your integration from becoming the bottleneck in the analytics pipeline.

The Trap: Requesting more than 10 groupBy dimensions in a single query. The query planner has a hard limit on dimension cardinality. Exceeding it triggers a 400 Bad Request with groupByDimensionLimitExceeded. The engine cannot materialize cross-product joins beyond a certain complexity without exhausting memory on the aggregation nodes.

Architectural Reasoning: Each additional dimension multiplies the number of buckets the engine must track. A three-dimension group by may generate thousands of unique combinations. The aggregation service allocates fixed memory per query. When the bucket count exceeds the threshold, the service rejects the request to protect cluster stability. Design your aggregation queries to use two or three dimensions maximum. If you require additional segmentation, perform sequential queries and join the results in your data warehouse. This respects the platform’s memory boundaries and ensures consistent latency.

Validation, Edge Cases & Troubleshooting

Edge Case 1: Annotation Indexing Lag and Real-Time Query Mismatches

The Failure Condition: An annotation is created via the UI or API. A subsequent Analytics API query returns zero results for that annotation, even though the recording exists and the time range is correct.
The Root Cause: The analytics index operates on a micro-batch commit cycle. Annotations are written to the document store immediately, but the analytics projection updates asynchronously. The default indexing lag ranges from 15 to 45 minutes depending on cluster load and annotation volume.
The Solution: Implement a reconciliation delay in your integration workflow. Do not query the Analytics API immediately after annotation creation. Queue the query for execution after a configurable delay. If real-time verification is required, fall back to the Recording Management API (GET /api/v2/recordings/{recordingId}/annotations) for immediate consistency, then poll the Analytics API for batch processing later.

Edge Case 2: Filter Expression Syntax Collisions and Boolean Operator Precedence

The Failure Condition: A query containing mixed AND and OR operators returns unexpected record sets. Bookmarks matching one value appear alongside recordings that should have been excluded.
The Root Cause: The query parser applies strict operator precedence. AND binds tighter than OR. Parentheses are required to override default evaluation order. The expression annotationValue:'A' OR annotationValue:'B' AND annotationType:'bookmark' evaluates as annotationValue:'A' OR (annotationValue:'B' AND annotationType:'bookmark').
The Solution: Always wrap complex boolean logic in explicit parentheses. Use the format (annotationValue:'A' OR annotationValue:'B') AND annotationType:'bookmark'. Validate your query string against the platform’s syntax validator before deployment. Implement a pre-flight test query with a narrow time range to verify the result set matches expectations before scaling to production windows.

Edge Case 3: Pagination Boundary Conditions and Duplicate Record Retrieval

The Failure Condition: The final page of a paginated query returns records that already appeared on the previous page. The total count in the response exceeds the sum of individual page items.
The Root Cause: The analytics index allows concurrent writes. If annotations are updated or new bookmarks are added while your pagination loop is executing, the underlying dataset shifts. The cursor points to a document ID that may now be preceded by newly inserted records with identical sort keys.
The Solution: Implement idempotent deduplication using the recordingId as the unique key. Maintain a hash set of processed recording IDs. Skip any ID that already exists in the set. Additionally, freeze the dataset by querying a fixed historical window rather than a rolling now() range. If you must query live data, accept that duplicates may occur and design your downstream consumer to handle them gracefully. Do not assume strict transactional isolation across pagination boundaries.

Official References