Mailster often imports CSV files with many fields slowly because the import process must parse every column, even if most are empty. While Mailster lets users map and ignore unused fields, leaving 30 columns (especially variable usage across imports) causes the importer to treat every field as potentially relevant, so it parses and processes each, even when empty, to keep data structure consistent for each new row.
Why It’s Slow
- Header Processing: On each import, Mailster reads all column headers and builds mappings for every single field, which takes time regardless of how many rows have actual data.
- Row Parsing: Every row is parsed in full, with Mailster checking for empty values, existing records, and field-level data. Empty fields still require a check to determine if a value should be updated, skipped, or set to null.
- Field Mapping Complexity: If imported files vary (some have all 30 fields filled, others almost none), Mailster cannot ignore fields automatically. The import logic still processes “empty” columns to maintain compatibility with future imports where data might be present.
- Database Overhead: Even when fields are empty, the importer updates or inserts data row-by-row, which is inherently slower on large files with many columns.
Implications
- Skipping field mapping (by ignoring empty columns) could increase speed but creates issues if later imports have data in these columns.
- The only way to speed up imports without ignoring columns would be optimizing your server resources (memory and CPU), reducing simultaneous site traffic during imports, or splitting the import into smaller files if practical.
This behavior is common to other importers as well; processing many columns—even empty—creates both parsing and mapping overhead to preserve structure and future compatibility.Mailster takes a long time to import CSV files with many fields—even when most fields are empty—because the importer must still process every column and row for each entry, not just the non-empty ones. Even if many fields are blank, Mailster performs field mapping, checks for existing records, and runs through its full validity and import logic on all 30 fields for every row. It cannot easily optimize by ignoring unused columns if you haven’t specified to skip them, since future files might contain valid data in any of those fields.
This means:
- Every mapped field is parsed, validated, and inserted or updated, even when blank.
- The more columns in your CSV, the longer the import takes, as Mailster needs to build and maintain all relational and subscriber data structures, regardless of whether entries are empty or present.
- This is compounded by Mailster’s need to check for duplicates, existing users, and potential row-level issues on each import to protect data integrity.
In bulk import systems (not just Mailster), large column counts—even with lots of empty data—significantly slow down parsing and row-by-row database operations, since the importer cannot reliably know what will be important in every file you upload.Mailster’s import process can be slow with CSV files containing many columns—even if most are empty—because it parses and maps every field for each row, regardless of content. When fields aren’t explicitly ignored, Mailster must fully process each column to maintain compatibility for files where those fields might eventually be filled. This means every row goes through unnecessary mapping and validation, increasing overhead and reducing performance, especially with 30 columns and many rows.
Mailster also checks each field for validity, potential updates to existing subscribers, or merging information, regardless of whether a value is present. This standard import logic, found in most bulk CSV importers, leads to longer processing times when handling wide CSVs with many empty fields unless optimizations—such as ignoring unneeded columns or splitting large files—are possible.
Essentially, Mailster’s slow import in this setup stems from:
- Full mapping and checking of every included column
- Row-by-row examination and row database operation overhead even for empty values
- Retaining flexibility for future imports where all fields may be used.
Mailster’s import speed issues, even with increased memory, largely reflect common design trade-offs seen in many WordPress email marketing plugins and bulk importers—not just Mailster. These systems prioritize compatibility and data integrity for a wide range of use cases (variable subscriber fields, validation, deduplication, plugin/plugin conflicts) rather than raw performance, especially on typical shared hosting.
Why Performance Remains Slow
- WordPress PHP Execution: Most imports are still limited by PHP’s single-threaded nature in web environments—memory upgrades help, but the process is still I/O and CPU bound. Large, multi-column files with many blank/nullable entries slow things down due to PHP’s built-in array handling and the overhead of multiple database operations per row.
- Validation & Hooks: Mailster validates, logs, and may call hooks or additional plugins with each entry, further lengthening processing time, even if system memory is ample.
- No Background Worker: Most WordPress plugins run imports via synchronous admin-ajax calls or manual triggers, so background processing and true multi-threading is rare unless you move jobs to a dedicated background task runner or queue.
Limitations of Increasing Memory
- Memory vs. Single Thread: 3GB of memory won’t speed up a process that is CPU-bound or held up by the PHP/WordPress request lifecycle; it mostly helps prevent out-of-memory errors on very large files.
- Database Write Bottlenecks: Even with more memory, database disk write speed, server I/O, and PHP’s sequential processing are common bottlenecks for “wide” imports.
Alternatives and Workarounds
- Pre-process CSVs: If possible, pre-clean files to remove consistently empty columns, splitting truly required fields into smaller batches when importing large datasets.
- WP-CLI Imports: Use WP-CLI where supported, as command-line imports often bypass web timeouts and are less affected by PHP execution limits.
- Consider Dedicated Tools: For large or frequent imports, consider specialized database import tools or ETL scripts, which are orders of magnitude faster than most plugin-based importers.
Mailster is not unique in this regard; the issue is mostly a byproduct of the constraints and generalist approach of the WordPress ecosystem.The slow import speed with Mailster is not just a result of memory limits, but is typical of how many WordPress-based importers handle wide CSVs. Mailster’s design prioritizes compatibility and record integrity, leading to excessive resource and time use when processing every field—even when most are empty—because it must validate, map, and write all columns for every row. Increasing PHP/server memory (like setting 3GB) only helps prevent out-of-memory errors, but does not address bottlenecks in PHP’s single-threaded execution, row-by-row validation, and database I/O, which are the real constraints.
Mailster is not alone—WordPress plugin importers often lack the optimization and multithreading you’d find in purpose-built ETL tools, background workers, or scripts outside the WordPress architecture. For massive or frequent imports, pre-cleaning the CSV to remove unused columns or using WP-CLI/background jobs are best-practice workarounds until more efficient import engines are adopted in the plugin ecosystem.