Marketing Center Hierarchy Sync in Practice: A Single-Strategy Rollout from Feishu Sheets to MySQL

What This Strategy Solves

In a retail enterprise, the marketing center hierarchy table (channel type, distribution type, department, e-commerce channel type, online platform, etc.) is maintained by business users in a Feishu spreadsheet. Downstream order, rebate, and commission settlement systems all depend on this table; if changes in the sheet are not propagated to the business database in time, settlement rules diverge. We use the Qeasy Data Integration Platform to pull the Feishu sheet into MySQL on a daily basis, ensuring a single source of truth for downstream consumption.

Data Flow and Field Mapping

The flow is one-way: Feishu spreadsheet (source) → Qeasy intermediate layer → MySQL business database (target).

The source is the Feishu Open Platform endpoint GET /open-apis/sheets/v2/spreadsheets/:spreadsheetToken/values/:range, with valueRenderOption=ToString and dateTimeRenderOption=FormattedString to force all cells to strings and avoid implicit type conversion across systems.

The target is a MySQL batchexecute SQL write, primary key id, with idCheck=true to enforce idempotency via the primary key.

Key field mapping:

How to Configure It on Qeasy

In the Qeasy Data Integration Platform, we configure this strategy as a standard pipeline: source connector + target connector + field mapping + scheduling.

Source: Feishu connector. Pick a QUERY WebAPI, set the primary key field to id, and turn off idCheck because the source is a sheet and we don't dedupe there. Set buildModel=false, autoFillResponse=true so the response shape flows downstream automatically. Pass valueRenderOption and dateTimeRenderOption as fixed request parameters. Set headers_line=1 so the platform knows the first row is the header, not data.

Target: MySQL connector. Pick an EXECUTE SQL API, primary key id, and turn idCheck on—this is the core of idempotency; reruns will not corrupt data. All fields in the SQL template use {{field}} placeholders that bind directly to source fields.

Field mapping. The Qeasy mapping panel binds columns one-to-one by name. For master-data strategies like this, field names are usually pre-agreed, so we centralize code mapping in the platform. To change the channel code dictionary later, you edit one place and don't dig into scripts.

Scheduling. Set the source cron to 0 1 * * * (extract at 01:00), and the target cron to 30 1 * * * (write at 01:30), leaving a 30-minute buffer for extraction and stability.

Implementation Steps

We split this strategy into three phases:

Phase 1: Increment starting point. Create the target table and indexes in MySQL first, with primary key id. Freeze the Feishu sheet as a baseline version, run a full pull through Qeasy, and complete the initial load as the comparison starting point.

Phase 2: Full trigger. After headers_line=1 is enabled, write to the target table in full-overwrite mode. Confirm the MySQL side accepts truncate-insert semantics, or fall back to INSERT ... ON DUPLICATE KEY UPDATE.

Phase 3: Schedule frequency and monitoring. Run daily at 01:00 in production. Attach "success rows / failure rows" alerts on Qeasy for this strategy; failure details land in a log table. A common pattern among Qeasy customers is the "incremental + full dual track": incremental on weekdays, a full-reload fallback on weekends, so missed rows don't pile up.

Lessons Learned

1. Don't rely on default date serialization. Feishu returns dates as timestamps by default; writing them straight into MySQL yields numbers like 44927. Always explicitly set valueRenderOption and dateTimeRenderOption to formatted strings on the source side; don't gamble on defaults.

2. Header rows treated as data. The first row of a Feishu sheet is the header. If headers_line is not explicitly set, the platform treats the header as a dirty row and writes it into the id field, causing a primary key conflict and breaking the entire pipeline.

3. idCheck means different things on source and target. Source idCheck is off because spreadsheets don't dedupe; target idCheck must be on, otherwise reruns produce duplicates. This is a typical mistake—understand it per endpoint and don't copy-paste blindly.

4. Centralize code mapping. Dictionary fields like channel type and e-commerce channel type are often edited by business users with inconsistent casing or labels (e.g., "offline / online / other"). A common Qeasy pattern is to maintain dictionary mapping centrally: when new values appear at the source, route them to a "pending confirmation" table first; only after confirmation do they flow to the main table, so dirty data doesn't pollute downstream settlement.

5. Spaces and full-width characters in fields. The 部门 field in Feishu is often filled with values like 销售部 or 销售部 , which break joins after loading. The safe approach is to attach a trim + full-width-to-half-width cleansing step in the Qeasy mapping.

When This Applies and When It Doesn't

Applies: lightweight sync scenarios where master data such as organizations, categories, or price tiers is maintained by business users in Feishu and needs to land in MySQL daily or hourly for downstream order, settlement, or analytics consumption. Does not apply: high-frequency writes (sub-minute), transactional consistency, or multi-table cascading writes—those should go through an event bus or a dedicated middleware, not a sheet sync.