Ssis-948 Repack

| Symptom | Root Cause | Remedy | |---------|------------|--------| | | Destination log file is filling up because the recovery model is FULL and log backups are not occurring during the run. | Switch the target to BULK_LOGGED for the duration of the load or schedule a log‑backup every 10 min. | | “Deadlock victim” errors | Parallel writers are contending on a non‑clustered index that is being updated for each row. | Add a filtered index that excludes the load column, or temporarily drop the index and rebuild after the load. | | “Invalid column name” | Mismatch between source metadata and the destination table schema (e.g., a newly added column). | Refresh the Data Flow metadata or use the ValidateExternalMetadata = False property and map columns manually. | | Chunk size never grows | Destination table has a high page split rate; the engine keeps shrinking chunks to avoid log growth. | Re‑organize/re‑build the table’s clustered index before the load, or set a fixed ChunkSize if you know the optimal batch. | | High CPU usage on the SSIS host | MaxParallelism is set higher than the physical cores, causing context‑switch thrashing. | Set MaxParallelism ≤ Number of logical CPUs . |

Fault Tolerance & Checkpointing

Select and then reinstall a fresh copy, as repairing might not fix corrupted VSIX files. 2. Clear Visual Studio Component Cache ssis-948

| Requirement | How SSIS‑948 Helps | |------------|---------------------| | (hundreds of millions to billions) | Dynamically breaks the load into optimal “chunks” (default 10 000 rows) that are sized based on target table indexes, memory pressure, and transaction log throughput. | | Minimal impact on source systems | Uses asynchronous read‑ahead and pipeline‑back‑pressure to keep the source connection open only for the time needed to fill the next chunk, dramatically reducing lock time on the source. | | High‑throughput network environments | Leverages Multiple Active Result Sets (MARS) and batch‑insert ( INSERT … VALUES (…) , (…) , … ) for up to 1 000 rows per round‑trip, automatically falling back to tabular‑direct bulk‑copy when the network latency exceeds a configurable threshold. | | Transactional safety | Each chunk runs inside its own autocommit transaction, with an optional save‑point mode that allows you to roll back only the offending chunk rather than the whole batch. | | Built‑in data‑quality checks | Offers declarative pre‑load validation rules (null‑ability, range checks, foreign‑key existence) that are evaluated in‑flight without a separate data‑flow path. Invalid rows are diverted to a configurable Error Output (flat file, Azure Blob, or a staging table). | | Scalability on modern hardware | Detects the number of logical processors and automatically spawns parallel writer threads (up to MAXDOP ‑configured value) that write to the same destination table using partition‑aware bulk‑copy, ensuring minimal latch contention. | | Symptom | Root Cause | Remedy |