The append refresh mode incrementally adds new rows to the acceleration on each refresh. It is designed for append-only or immutable datasets such as time-series, event, and log data.
Use append when:
full mode on each interval.append mode requires a time_column (opens in a new tab) that identifies new rows by comparing the local maximum value to the source. Data is incrementally refreshed where time_column in the source is greater than max(time_column) in the acceleration — see Day-Granular Time Columns (opens in a new tab) for the one case where that comparison is inclusive instead.
To account for clock skew or late-arriving rows, configure an overlap window with acceleration.refresh_append_overlap (opens in a new tab). Rows within the overlap are re-read on each refresh.
A date-typed time_column (Arrow Date32, configured as time_format: date (opens in a new tab)) carries no time of day, so every row of a given day shares one value. A strictly-greater comparison against the accelerated maximum would exclude every row that arrives later for the day already loaded, on that refresh and on every one after it.
For a day-granular time column the comparison is therefore inclusive, against the start of the high-water mark's day, and Spice's exact-row de-duplication drops the already-loaded rows that come back so nothing is appended twice. A time_partition_column of the same type is floored to that same day boundary, so the partition predicate does not exclude the day the new rows are in.
The trade-off is that each refresh re-reads the boundary day from the source rather than nothing — the minimum needed to see that day's new rows at all. Sub-day time columns are unaffected and keep the strict comparison; if a single day is large, bound the re-read by using a sub-day time_column.
refresh_append_overlap still subtracts its duration from the high-water mark, but on a day-granular column the resulting window is quantized to the day boundary, so an overlap shorter than a day re-reads the whole boundary day.
time_partition_columnDatasets partitioned by a less-granular time column (day, month, year) can specify time_partition_column (opens in a new tab) in addition to time_column for efficient partition pruning at the source.
For object-store sources, set time_column or time_partition_column to the special value last_modified to append only newly created or updated files. Spice uses file metadata to determine which files are new, dramatically reducing scan time for large datasets.
If last_modified exists as a column in the data, the column value takes precedence over file metadata.
This is supported for connectors that accept the file format parameter (opens in a new tab), such as s3://, abfs://, and file://.
Append-mode accelerations that define a time_column wait to report ready until the first append refresh completes after snapshot bootstrap (opens in a new tab). This keeps the dataset out of rotation until the freshest data is available while still benefiting from snapshot-assisted startup.
Pair refresh_mode: append with a primary_key and on_conflict: upsert to handle source rows that are occasionally updated. See End-to-End Incremental Ingestion Example (opens in a new tab).
On Spice Cayenne (opens in a new tab), duplicate keys are collapsed inside one incoming batch. The same key in a later batch of that refresh is rejected. When one poll can contain two versions of a key — a cold load of a log, or a refresh_append_overlap (opens in a new tab) window that holds both — append without a primary_key and read the latest row from a view. DuckDB, which the example above uses, upserts across batches.
An append poll of an Iceberg source skips files and Parquet row groups from partition values and column statistics. How much it skips depends on whether the table is partitioned or sorted by the time column, or compacted so that old and new rows share a file. See Append refresh and table layout (opens in a new tab).