Speed and Secure MySQL
Unload, Load, Reorg, Query, Mask, and Test
You may face one or more of these time-consuming issues working with MySQL:
- Data discovery: profiling, classification, ERDs
- Unloading and loading large tables
- Routine utility operations (reorgs)
- Complex queries
- Migration or replication
- Access and activity control, monitoring and audit (firewall)
- Masking sensitive data
- Generating smart, safe test data
Specific performance diagnoses and tuning also take time and may affect other users. Finally, stored SQL procedures may also be programmed inefficiently, require optimization, then still take too long to run.
Speed MySQL Unloads
Specify SELECT * from the table so you do not encumber the unload with qualifiers like distinct, order by, and group by. But once that data are in the flat file, use the SortCL program in IRI CoSort to de-dupe, sort, join, group (and report on) the extracts far faster ... in parallel, outside the database.
Speed MySQL Loads
IRI CoSort to pre-sort flat files for bulk bcp imports.
Create the clustered index first and pre-sort the input files (on the primary index key) to shorten the create index step. Use the SORTED_DATA option when creating indexes to reflect that CoSort bypassed the slower sort in bcp.
Speed MySQL Reorgs and Queries
Externalize SQL Transformations
IRI CoSort to relieve the database of processing overhead. Leverage file system I/O, multi-threading, and the proven data transformation power and consolidation of the CoSort SortCL program.
Migrate and Replicate MySQL Databases
Mask Data in MySQL Columns
IRI FieldShield to mask sensitive data in MySQL, like personally identifiable information (PII) or protected health information (PHI), FieldShield applies masking, encryption, and other de-identifying functions to one or more columns at a time. Use FieldShield to comply with privacy laws like HIPAA, PCI DSS, FERPA, and GDPR.
Generate MySQL Test Data
IRI RowGen to rapidly populate MySQL operations with safe test data. RowGen uses your data models to automatically generate the test data for an entire database with referential integrity.