Dm Limits And Best Practices

cognitedata/builder-skills/skills/dm-limits-and-best-practices

作者 cognitedata7758093d8656无许可证6 个星标收录于 2026年10月8日更新于 2026年10月8日仓库2天前更新

CDF Data Modeling limits: concurrency (429s), pagination, batching, search vs filter, QueuedTaskRunner, and the cap on LLM calls over query results (5 default, 50 max, cached). Triggers: DMS limits, 429, pagination, nextCursor, cdfTaskRunner, instances.query, chat completions over query results.

AI 生成的概览

编写 CDF 数据建模 API 代码的参考指南,涵盖并发、分页、批处理与 LLM 调用上限。

功能
该参考技能提供调用 Cognite 数据建模 API 的模式与代码片段。内容包括通过全局 QueuedTaskRunner 信号量控制并发与应对 429、使用游标分页、upsert 与 delete 的批处理、in 过滤器 1000 个值的上限,以及 search 与 filter 的选用场景。它还给出对查询结果调用 LLM 的硬性上限,并列出嵌套信号量死锁等常见陷阱。
适用场景
适用于编写或审查调用 CDF 数据建模 API 的代码,需要避免限流、因缺少分页而丢数据或请求过大的场景。也适用于在 instances.search 与精确匹配 filter 之间做选择,或为查询结果添加 LLM 调用时。
运行要求
不附带脚本,仅为说明与代码示例。使用前提是具备 Cognite CDF 项目与数据建模 API、CogniteClient,并能访问 CDF API 及所链接的 Cognite 文档。若项目中缺少信号量工具,可能需要自行创建。

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.

This skill owns runtime reliability concerns: limits, concurrency, retries, throughput, and batching behavior. For traversal payload correctness and graph-specific failure signatures, see dm-graph-traversal.


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.
typescript
// 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:

typescript
// Text search + exact filter: search for "pump" but only in active nodesconst 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.

typescript
// Exact match: get all completed batchesconst 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

NeedUse
User typing in a search boxinstances.search with OR
Find a specific item by nameinstances.search with AND
Filter by status, date range, enumsfilter on list/query
Text search + exact constraintsinstances.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.

typescript
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:

typescript
/** * 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():

typescript
import { cdfTaskRunner } from '../../../../shared/utils/semaphore';
// Single queryexport 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):

typescript
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

typescript
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<string, string> (one cursor per table expression). Use it via the cursors parameter:

typescript
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:

typescript
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

typescript
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

typescript
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:

typescript
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,        }))      );    });  }}

Hard gate — LLM calls over query results

Do not map chat completions over instances.list / query / search hits. Prefer one Atlas / EOS sidebar turn (integrate-fusion-agent). If per-item completions are required: 5 per user action, ceiling 50, cache by space:externalId:lastUpdatedTime, user-initiated only.


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.

typescript
// BAD: Nested semaphore — can deadlockasync 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 semaphoreasync 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:

typescript
// BAD: May miss dataconst response = await client.instances.list({ limit: 1000, /* ... */ });const items = response.items; // Could be incomplete!
// GOOD: Paginateconst 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:

typescript
// BAD: Too many simultaneous requestsawait Promise.all(batchIds.map((id) => client.instances.query({ /* ... */ })));
// GOOD: Each call goes through the semaphoreawait 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
  • LLM-over-query-results capped (5 / max 50) and cached, or not present
  • Refer to https://docs.cognite.com/cdf/dm/dm_reference/dm_limits_and_restrictions for current limits

来源与署名

来源:cognitedata/builder-skills位于skills/dm-limits-and-best-practices提交7758093

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