# ERP API Initial Data Sync Duration Calculator

> Source: https://netray.co/tools/erp-api-initial-data-sync-duration-calculator
> Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems.
> Section: Free Tools

## Short answer

Computes how many pages, how many hours and how many quota days an initial extract from an ERP REST API will take, with a page-size what-if.

## Category

developer

## Audience

- Integration developer
- ERP admin
- Data engineer

## Tool

**Kind:** calculator

**Inputs:**

#### Records to extract

**Key:** records

**Type:** number

**Default:** 2500000

**Min:** 1

**Step:** 10000

**Unit:** records

#### Page size

**Key:** page

**Type:** slider

**Default:** 500

**Min:** 10

**Max:** 5000

**Step:** 10

**Unit:** records per call

**Help:** Respect the API maximum page size.

#### Average latency per call

**Key:** latency

**Type:** number

**Default:** 800

**Min:** 1

**Step:** 50

**Unit:** ms

**Help:** Grows with page size on most ERPs. Measure with your chosen size.

#### Parallel workers

**Key:** workers

**Type:** slider

**Default:** 4

**Min:** 1

**Max:** 32

**Step:** 1

**Unit:** workers

#### Rate limit

**Key:** rate

**Type:** number

**Default:** 10

**Min:** 0.1

**Step:** 0.5

**Unit:** calls per second

**Help:** Hard cap enforced by the ERP or gateway.

#### Daily call quota

**Key:** quota

**Type:** number

**Default:** 100000

**Min:** 1

**Step:** 5000

**Unit:** calls per day

**Outputs:**

#### API calls needed

**Key:** pages

**Expr:** ceil(records / page)

**Format:** integer

#### Worker throughput

**Key:** worker_rate

**Expr:** workers * 1000 / latency

**Format:** decimal

**Description:** Calls per second the workers can produce.

#### Effective rate

**Key:** eff_rate

**Expr:** min(rate, worker_rate)

**Format:** decimal

**Description:** Whichever of rate limit or workers is the bottleneck.

#### Wall-clock time at effective rate

**Key:** hours

**Expr:** pages / max(eff_rate, 0.000001) / 3600

**Format:** hours

**Primary:** true

#### Days needed by quota

**Key:** quota_days

**Expr:** ceil(pages / quota)

**Format:** days

**Primary:** true

#### Records per second

**Key:** rec_sec

**Expr:** eff_rate * page

**Format:** number

**Chart:**

#### Extraction hours as page size grows (latency held constant)

**Type:** line

**X:** page

**From:** 50

**To:** 5000

**Steps:** 30

**Series:**

- hours

**Disclaimer:** Latency usually rises with page size, so the chart is optimistic at large pages. Re-measure latency at each size.

## Content

**Intro:** Before promising a go-live date for a data feed, work out how long the first full pull takes. Rate limits, page sizes and daily quotas all cap throughput, and the slowest one wins.

**Sections:**

#### How to use it

**Body:** Enter the record count from a SELECT COUNT on the source object, the page size you plan to use and the latency you measured at that size. Add worker count, the published rate limit and any daily call quota. The tool computes pages, the effective call rate (the lesser of the limit and what workers can produce) and total hours. A separate line shows the days needed if a daily quota is the true ceiling, which is common with cloud ERP APIs.

#### Formula

**Body:** Pages equal records divided by page size, rounded up. Worker throughput is workers times 1000 divided by latency in milliseconds. Effective rate is the minimum of that and the rate limit. Hours equal pages divided by effective rate divided by 3600. Quota days equal pages divided by the daily quota, rounded up. The larger of the hours-based and quota-based durations is your plan.

**Bullets:**

- More workers do nothing once the rate limit is the bottleneck.
- Bigger pages cut calls but raise latency and memory.
- Keyset or cursor paging avoids slow deep OFFSET scans.

#### ERP notes and mistakes

**Body:** Run extracts off-peak so you do not starve interactive users of the same limit. Do not assume the published page maximum is fast: some ERPs slow sharply on wide objects with many child lines. Plan for retries and for the delta catch-up after the initial load finishes. Where an API is too slow, consider the vendor's bulk export, a database replica or a file-based interface instead of pushing the REST limit.

**Cta:** Netray builds ERP connectors and read-only data pipelines for private AI. If your first load is blocked by limits, we can look at alternatives with you.

**Faq:**

#### Should I always use the maximum page size?

**Answer:** Not necessarily. Larger pages reduce call count but raise per-call latency, memory use and the cost of a failed call. Test two or three sizes and pick the one with the best records per second that still retries cleanly.

#### Why does adding workers not speed things up?

**Answer:** Because a rate limit or concurrency cap on the ERP side caps total calls per second. Once workers can produce more than the limit, the effective rate equals the limit and extra workers only queue.

#### How do I get the record count?

**Answer:** Use a count query or the API's total count header or parameter if it offers one. For large objects, an approximate count from database statistics is enough for planning.

#### What about incremental sync after the initial load?

**Answer:** Use a modified-date filter or change tracking and run it on a schedule. The incremental volume is usually a small fraction, so the same calculator with changed record counts sizes it.

## Search terms

erp api bulk extract time, initial load rest api duration, api pagination throughput calculator, erp data sync time estimate, rate limited api extraction time, how long to sync erp data via api

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