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Operating dashboards need to open quickly, while key metrics must reflect business changes. Focusing only on cache hit rates can obscure the time represented by results. Scanning source databases again for every request can also make repeated analysis consume resources continuously.
HENGSHI gives enterprises configurable capabilities for chart-data caching, dataset acceleration, and data updates. Projects can choose query paths according to freshness requirements, with data teams maintaining update schedules and metric definitions. Understanding the role of each capability is more useful for delivery than broadly claiming that enabling caching makes everything faster.
Start by distinguishing three objects: chart caches store query results, acceleration engines hold processed data, and historical Q&A records retain earlier analyses. Their update conditions differ. One mechanism cannot be generalized to the entire ChatBI chain.
1. Available Acceleration and Refresh Mechanisms
| Mechanism | HENGSHI capability | Boundary to clarify |
|---|---|---|
| Chart-data cache | Applications can configure the chart-data cache period; charts without a separate setting follow the system configuration. | Check the effective period and query conditions for the chart. This is not an answer cache for an entire conversation. |
| Chart refresh | Refresh can bypass the chart cache and issue a new data query. | If the data comes from an acceleration engine, the query still reads that engine and may not include the latest source records. |
| Dataset acceleration | Store dataset processing results in the chosen acceleration engine to reduce repeated complex processing. | Plan data updates at the same time, and verify the accelerated data scope and authorization configuration. |
| Data-update management | Immediately update datasets imported into an engine, or configure schedules and inspect update status and records. | Source changes, engine updates, and page queries are separate stages and require separate checks. |
The public application-settings manual lists a default chart-cache period of 3,600 seconds, or one hour, and supports application-level configuration. A deployment may have changed this value, so projects should check the effective setting. Application settings, chart operations, and performance guidance describe how to use refresh and acceleration.
2. Verify Freshness Along the Actual Data Path
Between a source-database update and a user seeing new data, there may be synchronization schedules, data in an acceleration engine, and chart caches. Administrators should establish which layer the analysis reads before deciding what operation is needed.
| Change or requirement | What to inspect | Operations to distinguish |
|---|---|---|
| New business records are added to the source | Whether the dataset is imported into an engine, relevant update jobs succeeded, and the chart queried again. | Refreshing a chart does not necessarily update engine data first. |
| Source-table field names or types change | Dataset structure and dependent fields, metrics, and charts. | Updating the schema synchronizes structural information; it does not establish that all business data has refreshed. |
| Metric calculation rules change | Metric definitions, the analytics objects using them, and actual query results. | Verify results again instead of merely waiting for the cache period to expire. |
| User authorization or organizational scope changes | Current identity, resource authorization, data-permission mode, and access results. | A previously returned result does not establish that the identity may keep viewing it. |
HENGSHI’s dataset-update functionality supports inspecting status, records, and recent updates, and configuring schedules for relevant datasets. Teams can include update completion in routine checks, then query under the same conditions to verify that new data has entered the analysis. Confirm the specific update method and supported scope for each dataset type.
Numbers in historical messages must be understood in the conditions under which they were generated. Requery key metrics for operational decisions, checking the data batch and time range. Opening an old answer again does not mean a new query ran.
3. Maintain Business Semantics Before Reusing Results
“This month’s sales” and “this month’s revenue” may sound similar but represent different enterprise rules: tax treatment, refunds, and whether aggregation follows order placement or revenue recognition. Teams should confirm that terms actually refer to the same metric before discussing repeated computation and reuse.
HENGSHI’s metrics and HQL analysis capabilities provide objects for maintaining these definitions. Data Agent can use dataset knowledge and business context to find resources, while data teams can inspect metrics and queries to verify results. Reuse also requires matching data scope, filters, reporting time, and valid authorization.
If an enterprise adds result caching in its own integration layer, the design should account for tenants, execution identities, permission changes, and data versions. This is integration guidance. It does not mean HENGSHI has published a particular universal cache-key format or that normal data authorization can be bypassed.
4. Choose Freshness and Computation Costs for the Business
| Business need | Possible implementation | What the business owner must confirm |
|---|---|---|
| Reviewing stable historical periods | Maintain verified data batches and set a cache period appropriate for repeated access. | Whether historical periods still receive late entries, reversals, or adjustments, and how recalculation follows changes. |
| Daily sales analysis | Align data-update schedules with the business closing cycle, then arrange dashboard queries. | Data cutoff time, the latest successful update, and failure handling. |
| Inventory or other analysis requiring recent changes | Assess direct connections where source capacity permits; clarify delays when using synchronization or engines. | Acceptable delay and the effect of peak queries on source systems. |
| Complex cross-source analysis | Integrate data or materialize some complex processing first, then configure subsequent analysis. | Whether update cadence, calculation definitions, and data permissions align. |
Test speed and freshness together. After enabling an acceleration engine, inspect synchronized results. When querying directly, also observe source-database load. HENGSHI’s public performance FAQ discusses trade-offs between these paths.
5. A Retail Analytics Implementation Example
The following is a fictional solution example, not a real customer case or measured outcome. A retailer wants regional teams to review the previous day’s sales in morning meetings, while operations staff query refund changes during the current day. These questions have different freshness needs and should not share one update assumption.
The team first asks finance to confirm sales and refund definitions, then configures updates and cache periods for the previous-day dashboard. Each day, it checks whether update jobs completed and reconciles fixed stores and periods. For current-day refunds, it separately assesses query paths and acceptable delay, making the data cutoff clear at query time.
Regional and headquarters users perform acceptance checks with their own identities, verifying visible stores and metric results. This reuses HENGSHI’s existing metrics, charts, and update capabilities while translating scenario-specific freshness requirements into configuration and verification.
6. Validate Results After Changes Before Launch
- Data changes: add, correct, or delete test records, then check the relevant update process and new query results.
- Definition changes: after changing a test metric, verify dashboards and question variants that reference it. Do not assume all historical answers update automatically.
- Permission changes: switch roles or revoke authorization, then verify that normal entry points and external integrations still respect access boundaries.
- Cold/warm comparison: record initial and repeated queries, refresh behavior, and data cutoff times separately, avoiding a presentation of only one favorable run.
HENGSHI’s caching and acceleration capabilities give enterprises ways to control repeated computation and organize data updates. Teams need to manage metric definitions, data batches, query paths, and user identities together so that faster analytical results remain useful for business operations.