CDF Data Modeling: Limits, Concurrency & Best Practices This is a reference skill. When writing or reviewing code that calls CDF Data Modeling APIs, apply the patterns below. DMS Limits Reference For the latest concurrency limits, resource limits, and property value limits, see the official documentation: https://docs.cognite.com/cdf/dm/dm_reference/dm_limits_and_restrictions Key things to be aware of: Instance apply , delete , and query operations each have their own concurrent request limits Exceeding these limits returns 429 Too Many Requests Transformations consume a large portion of the concurrency budget, leaving less for other clients instances.list has a max page size (use pagination for complete results) instances.query table expressions each have their own item limit instances.upsert accepts up to 1000 items per call in filters accept at most 1000 values per expression; larger sets must be split into batches Search vs Filter: When to Use Which instances.search — Free-text search on text properties Use instances.search when you need fuzzy/text matching on string fields (names, descriptions, etc.). It supports an operator parameter: AND (default) — Narrow search. All terms must match. Use when the user provides a specific query. OR — Broad "shotgun" search. Any term can match. Use for exploratory/typeahead search where you want maximum recall. // Narrow search: find a specific cell by name (AND — all terms must match) const exactResults = await client . instances . search ( { view : { type : 'view' , ... PROCESS_CELL_VIEW } , query : 'reactor tank A' , properties : [ 'name' ] , operator : 'AND' , limit : 10 , } ) ; // Broad search: typeahead/autocomplete (OR — any term can match) const broadResults = await client . instances . search ( { view : { type : 'view' , ... BATCH_VIEW } , query : 'BUDE completed' , properties : [ 'name' , 'description' , 'batchStatus' ] , operator : 'OR' , limit : 10 , } ) ; You can combine search with filter to further constrain results with exact-match conditions: // Text search + exact filter: search for "pump" but only in active nodes const filtered = await client . instances . search ( { view : { type : 'view' , ... PROCESS_CELL_VIEW } , query : 'pump' , properties : [ 'name' , 'description' ] , filter : { equals : { property : getContainerProperty ( MY_CONTAINER , 'status' ) , value : 'active' , } , } , limit : 20 , } ) ; instances.list / instances.query with filter — Exact-match filtering Use filter when you need precise, deterministic matching (equals, range, in, hasData, etc.). No fuzzy matching — values must match exactly. // Exact match: get all completed batches const completedBatches = await client . instances . list ( { instanceType : 'node' , sources : [ { source : { type : 'view' , ... BATCH_VIEW } } ] , filter : { equals : { property : getContainerProperty ( BATCH_CONTAINER , 'batchStatus' ) , value : 'completed' , } , } , limit : 1000 , } ) ; Decision Guide Need Use User typing in a search box instances.search with OR Find a specific item by name instances.search with AND Filter by status, date range, enums filter on list/query Text search + exact constraints instances.search + filter in filter value limit (1000) and batching CDF in filters support a maximum of 1000 values in a single filter expression. If you need to filter against more than 1000 IDs, split values into chunks and issue multiple requests, then merge results. const IN_FILTER_BATCH_SIZE = 1000 ; // Reuse the Chunking Utility defined in the Batching Write Operations section. async function listByExternalIds ( client : CogniteClient , externalIds : string [ ] , ) : Promise < NodeOrEdge [ ]

{ const idBatches = chunk ( externalIds , IN_FILTER_BATCH_SIZE ) ; const responses = await Promise . all ( idBatches . map ( ( batch ) => cdfTaskRunner . schedule ( ( ) => client . instances . list ( { instanceType : 'node' , sources : [ { source : { type : 'view' , ... MY_VIEW } } ] , filter : { in : { property : [ 'node' , 'externalId' ] , values : batch , } , } , limit : 1000 , } ) ) ) ) ; return responses . flatMap ( ( r ) => r . items ) ; } QueuedTaskRunner (Semaphore) Always use the global cdfTaskRunner to wrap CDF API calls. It limits concurrent requests and prevents 429 errors and deadlocks. Source Code If the project does not already have a semaphore utility, create src/shared/utils/semaphore.ts with this implementation: /* * AbortError thrown when a queued task is cancelled / export class AbortError extends Error { public constructor ( message : string = 'Aborted' ) { super ( message ) ; this . name = 'AbortError' ; } } type PendingTask < AsyncFn , AsyncFnResult

= { resolve : ( result : AsyncFnResult ) => void ; reject : ( error : unknown ) => void ; fn : AsyncFn ; key ? : string ; } ; const DEFAULT_MAX_CONCURRENT_TASKS = 15 ; /* * QueuedTaskRunner for controlling concurrent operations * Used to limit concurrent CDF API requests to avoid rate limiting and deadlocks * Essentially a semaphore that allows a limited number of tasks to run at once. / export default class QueuedTaskRunner < AsyncFn extends ( ) => Promise < AsyncFnResult

, AsyncFnResult = Awaited < ReturnType < AsyncFn

,

{ private pendingTasks : PendingTask < AsyncFn , AsyncFnResult

[ ] = [ ] ; private currentPendingTasks : number = 0 ; private readonly maxConcurrentTasks : number = 1 ; public constructor ( maxConcurrentTasks : number = DEFAULT_MAX_CONCURRENT_TASKS ) { this . maxConcurrentTasks = maxConcurrentTasks ; } public schedule ( fn : AsyncFn , options : { key ? : string } = { } ) : Promise < AsyncFnResult

{ this . startTrackingTime ( ) ; return new Promise ( ( resolve , reject ) => { if ( options . key !== undefined ) { // Cancel existing tasks with the same key (deduplication) this . pendingTasks . filter ( ( task ) => task . key === options . key ) . forEach ( ( task ) => task . reject ( new AbortError ( ) ) ) ; this . pendingTasks = this . pendingTasks . filter ( ( task ) => task . key !== options . key ) ; } this . pendingTasks . push ( { resolve , reject , fn , key : options . key , } ) ; this . attemptConsumingNextTask ( ) ; } ) ; } public async attemptConsumingNextTask ( ) : Promise < void

{ if ( this . pendingTasks . length === 0 ) return ; if ( this . currentPendingTasks = this . maxConcurrentTasks ) return ; const pendingTask = this . pendingTasks . shift ( ) ; if ( pendingTask === undefined ) { throw new Error ( 'pendingTask is undefined, this should never happen' ) ; } this . currentPendingTasks ++ ; const { fn , resolve , reject } = pendingTask ; try { const result = await fn ( ) ; resolve ( result ) ; } catch ( e ) { reject ( e ) ; } finally { this . currentPendingTasks -- ; this . tick ( ) ; this . attemptConsumingNextTask ( ) ; } } public clearQueue = ( ) : void => { this . pendingTasks = [ ] ; } ; private startTime : number | null = null ; private startTrackingTime = ( ) : void => { if ( this . startTime === null ) { this . startTime = performance . now ( ) ; } } ; private tick = ( ) : void => { if ( this . pendingTasks . length === 0 ) { this . startTime = null ; } } ; } /* * Global task runner for CDF API requests * Limits concurrent requests to avoid 429 rate limiting and deadlocks / export const cdfTaskRunner = new QueuedTaskRunner ( DEFAULT_MAX_CONCURRENT_TASKS ) ; Usage Pattern Always wrap CDF calls with cdfTaskRunner.schedule() : import { cdfTaskRunner } from '../../../../shared/utils/semaphore' ; // Single query export async function fetchBatches ( client : CogniteClient ) : Promise < CDFBatch [ ]

{ return cdfTaskRunner . schedule ( async ( ) => { const response = await client . instances . query ( { with : { / ... / } , select : { / ... / } , } ) ; return response . items ?. nodes || [ ] ; } ) ; } // Multiple parallel queries (safe — the semaphore limits concurrency) export async function enrichBatch ( client : CogniteClient , batch : CDFBatch ) : Promise < BatchEnrichment

{ const [ currentOp , lastOp , cells , material ] = await Promise . all ( [ fetchCurrentOperation ( client , batch . space , batch . externalId ) , fetchLastCompletedOperation ( client , batch . space , batch . externalId ) , fetchProcessCells ( client , batch . space , batch . externalId ) , fetchMaterial ( client , batch . space , batch . externalId ) , ] ) ; return { currentOp , lastOp , cells , material } ; } // Each of the above functions internally uses cdfTaskRunner.schedule(), // so Promise.all is safe — the semaphore prevents exceeding concurrency limits Deduplication with Keys Use the key option to cancel stale requests when the same query is triggered again (e.g., user changes filters quickly): const result = await cdfTaskRunner . schedule ( async ( ) => client . instances . query ( { / ... / } ) , { key : batch-flow- ${ batchId } } ) ; // If another call with the same key arrives before this completes, // the previous pending call is rejected with AbortError Pagination DMS instances.list returns at most limit items and a nextCursor for the next page. DMS instances.query uses a cursors object keyed by table expression name. instances.list Pagination async function fetchAllNodes ( client : CogniteClient ) : Promise < CDFNodeResponse [ ]

{ const allItems : CDFNodeResponse [ ] = [ ] ; let cursor : string | undefined = undefined ; do { const response = await client . instances . list ( { instanceType : 'node' , sources : [ { source : { type : 'view' , ... MY_VIEW } } ] , filter : { equals : { property : getContainerProperty ( MY_CONTAINER , 'status' ) , value : 'active' , } , } , limit : 1000 , cursor , } ) ; allItems . push ( ... response . items ) ; cursor = response . nextCursor ; } while ( cursor ) ; return allItems ; } instances.query Pagination The query endpoint returns nextCursor as a Record (one cursor per table expression). Use it via the cursors parameter: import { isEmpty } from 'lodash' ; async function fetchAllResults ( client : CogniteClient ) : Promise < { results : CDFResult [ ] ; edges : EdgeDefinition [ ] }

{ const QUERY_LIMIT = 10_000 ; const fetchPage = async ( nextCursors ? : Record < string , string

) : Promise < { results : CDFResult [ ] ; edges : EdgeDefinition [ ] }

=> { const { items , nextCursor } = await client . instances . query ( { with : { results : { limit : QUERY_LIMIT , nodes : { filter : { hasData : [ { type : 'view' , ... RESULT_VIEW } ] , } , } , } , relatedEdges : { limit : QUERY_LIMIT , edges : { from : 'results' as const , maxDistance : 1 , direction : 'outwards' as const , filter : { equals : { property : [ 'edge' , 'type' ] , value : MY_EDGE_TYPE , } , } , } , } , } , cursors : nextCursors , // Pass cursors from previous page select : { results : { sources : [ { source : { type : 'view' , ... RESULT_VIEW } , properties : [ '*' ] } , ] , } , relatedEdges : { } , } , } ) ; const results = ( items ?. results || [ ] ) as CDFResult [ ] ; const edges = ( items ?. relatedEdges || [ ] ) . filter ( ( e ) => e . instanceType === 'edge' ) ; // Recurse if more pages exist if ( ! isEmpty ( nextCursor ) ) { const next = await fetchPage ( nextCursor ) ; return { results : [ ... results , ... next . results ] , edges : [ ... edges , ... next . edges ] , } ; } return { results , edges } ; } ; return fetchPage ( ) ; } Pagination + QueuedTaskRunner Combined Always wrap paginated fetches with the semaphore to avoid saturating the concurrency budget: export async function fetchAllWithPagination ( client : CogniteClient ) : Promise < CDFNodeResponse [ ]

{ return cdfTaskRunner . schedule ( async ( ) => { const allItems : CDFNodeResponse [ ] = [ ] ; let cursor : string | undefined = undefined ; do { const response = await client . instances . list ( { instanceType : 'node' , sources : [ { source : { type : 'view' , ... MY_VIEW } } ] , filter : { / ... / } , limit : 1000 , cursor , } ) ; allItems . push ( ... response . items ) ; cursor = response . nextCursor ; // Optional: break early if you have enough data if ( allItems . length = 500 ) break ; } while ( cursor ) ; return allItems ; } ) ; } Batching Write Operations When upserting many instances, chunk them to stay under the apply concurrency limit. Each instances.upsert call accepts up to 1000 items. Chunking Utility function chunk < T

( arr : T [ ] , size : number ) : T [ ] [ ] { const chunks : T [ ] [ ] = [ ] ; for ( let i = 0 ; i < arr . length ; i += size ) { chunks . push ( arr . slice ( i , i + size ) ) ; } return chunks ; } Batched Upsert with QueuedTaskRunner const UPSERT_BATCH_SIZE = 1000 ; async function batchUpsertNodes ( client : CogniteClient , nodes : NodeOrEdgeCreate [ ] ) : Promise < void

{ const chunks = chunk ( nodes , UPSERT_BATCH_SIZE ) ; // Process chunks through the semaphore — safe even with Promise.all await Promise . all ( chunks . map ( ( batch ) => cdfTaskRunner . schedule ( async ( ) => { await client . instances . upsert ( { items : batch , } ) ; } ) ) ) ; } Batched Delete with QueuedTaskRunner Instance deletes have an even stricter concurrency limit. Use a separate, more restrictive task runner: import QueuedTaskRunner from '../../../../shared/utils/semaphore' ; // Dedicated runner for deletes (stricter concurrency — check docs for current limit) const deleteTaskRunner = new QueuedTaskRunner ( 2 ) ; async function batchDeleteNodes ( client : CogniteClient , nodeIds : { space : string ; externalId : string } [ ] ) : Promise < void

{ const chunks = chunk ( nodeIds , 1000 ) ; for ( const batch of chunks ) { await deleteTaskRunner . schedule ( async ( ) => { await client . instances . delete ( batch . map ( ( id ) => ( { instanceType : 'node' as const , ... id , } ) ) ) ; } ) ; } } Common Pitfalls 1. Deadlocks from Nested Semaphore Calls If function A holds a semaphore slot and calls function B which also needs a slot, you can deadlock if all slots are occupied. Keep the semaphore at the outermost call level , or ensure inner calls don't go through the same semaphore. // BAD: Nested semaphore — can deadlock async function fetchAndEnrich ( client : CogniteClient ) { return cdfTaskRunner . schedule ( async ( ) => { const batches = await fetchBatches ( client ) ; // This also calls cdfTaskRunner.schedule! // If all slots are held by fetchAndEnrich callers, fetchBatches will never run } ) ; } // GOOD: Let inner functions own the semaphore async function fetchAndEnrich ( client : CogniteClient ) { const batches = await fetchBatches ( client ) ; // Has its own semaphore call const enriched = await Promise . all ( batches . map ( ( b ) => enrichBatch ( client , b ) ) // Each has its own semaphore call ) ; return enriched ; } 2. Forgetting Pagination DMS returns at most limit items. If you don't paginate, you silently lose data. Always check nextCursor : // BAD: May miss data const response = await client . instances . list ( { limit : 1000 , / ... / } ) ; const items = response . items ; // Could be incomplete! // GOOD: Paginate const allItems = [ ] ; let cursor ; do { const response = await client . instances . list ( { limit : 1000 , cursor , / ... / } ) ; allItems . push ( ... response . items ) ; cursor = response . nextCursor ; } while ( cursor ) ; 3. Unbounded Promise.all Without Semaphore Firing many parallel API calls will hit the 429 limit immediately: // BAD: Too many simultaneous requests await Promise . all ( batchIds . map ( ( id ) => client . instances . query ( { / ... / } ) ) ) ; // GOOD: Each call goes through the semaphore await Promise . all ( batchIds . map ( ( id ) => cdfTaskRunner . schedule ( ( ) => client . instances . query ( { / ... / } ) ) ) ) ; 4. Query Limit per Table Expression Each table expression in instances.query has its own limit . If your traversal might return more items than the limit in a single expression, you must paginate using the cursors parameter. 5. Oversized in Filters in filters are capped at 1000 values per expression. Passing more than 1000 values in a single in filter can fail or produce incomplete behavior depending on endpoint/version. Always chunk the values and run batched requests. Summary Checklist Wrap all CDF API calls with cdfTaskRunner.schedule() Paginate instances.list calls using cursor / nextCursor Paginate instances.query calls using cursors / nextCursor when data may exceed limits Chunk write operations to 1000 items per instances.upsert call Use a separate, stricter task runner for deletes Avoid nesting cdfTaskRunner.schedule() calls to prevent deadlocks Use Promise.all with semaphore-wrapped functions, never with raw API calls Use instances.search for text matching, filter for exact-match queries Split in filter values into batches of at most 1000 and merge responses Refer to https://docs.cognite.com/cdf/dm/dm_reference/dm_limits_and_restrictions for current limits

dm-limits-and-best-practices

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