MailWizz Subscriber Chunk Size and Campaign Throughput Optimization

OCTOBER 2025 · MAILWIZZ TECHNICAL REFERENCE

The subscribers_chunk_size setting determines how many subscriber records MailWizz loads into memory per processing iteration. This single value has a significant effect on both campaign throughput and server memory consumption.

What Chunk Size Controls

Each processing cycle, MailWizz queries the database for subscribers_chunk_size subscribers from the campaign list, checks each against the blacklist detection and delisting, and passes non-suppressed subscribers to the SMTP relay. A larger chunk size means fewer database round-trips per campaign completion, but more memory consumption per cycle.

Finding the Optimal Value

  • Start at 500 and measure: run a campaign and observe messages/minute throughput
  • Check PHP memory during sending: if workers approach the memory_limit, reduce chunk size
  • Check MySQL during sending: if query latency increases, reduce chunk size or increase InnoDB buffer pool
  • Increase to 1000-2000 if memory and database metrics show headroom

Blacklist Check Performance Impact

For each subscriber in the chunk, MailWizz performs a blacklist lookup. At chunk size 1000, that's 1000 database queries per cycle. Blacklist query latency is determined by the email index on mw_email_blacklist. At blacklist sizes above 1 million entries, ensure the index is optimal.

-- Verify index exists and is used
EXPLAIN SELECT * FROM mw_email_blacklist WHERE email = 'test@example.com';
-- type should be 'ref', key should be an email index

The relationship between chunk size and throughput is not linear. A chunk size of 1000 is not necessarily twice as fast as 500 — database contention and PHP memory pressure create diminishing returns. Test in increments of 200-300 and measure actual throughput at each level before increasing further.

Troubleshooting Common Issues

Production MailWizz deployments encounter predictable issues at predictable stages. Understanding the diagnostic workflow for the most common problems in this configuration area saves time and prevents the escalating complexity that comes from applying fixes to a misdiagnosed problem. The diagnostic approach is always the same: identify the symptom precisely (not just "it's not working"), isolate the layer where the failure occurs (MailWizz application, delivery server connection, DNS, ISP rejection), and fix at the correct layer.

Systematic Diagnosis Approach

Check MailWizz logs first (available in Backend → Misc → Application Logs), then check the delivery server SMTP logs, then check the PowerMTA accounting log. Most issues surface in one of these three places. A problem that does not appear in any of these logs is almost always a configuration issue — the system is not attempting what you expect it to attempt.

# MailWizz diagnostic log locations:
# Application logs: Backend → Misc → Application Logs
# Delivery logs: Backend → Campaigns → [Campaign] → Delivery Logs
# Bounce logs: Backend → Bounce Servers → [Server] → Logs

# Server-side logs:
# MailWizz application: /path/to/mailwizz/apps/common/runtime/application.log
# PowerMTA delivery: /var/log/pmta/pmta.log
# PowerMTA accounting: /var/log/pmta/accounting.csv

Performance Optimization for Production Scale

MailWizz performance at scale depends on three infrastructure layers: the web application server (PHP/nginx or Apache), the database (MySQL — query optimization is critical at high subscriber counts), and the delivery infrastructure (PowerMTA connection pool sizing). Performance problems in any of these layers manifest as slow campaign sends, delayed processing, or timeouts that appear unrelated to the specific configuration area being managed.

The most common performance constraint in production MailWizz environments is MySQL query efficiency. As subscriber lists grow beyond 500,000 records, unoptimized database queries for segmentation, bounce processing configuration, and campaign statistics become significant bottlenecks. Ensure that subscriber tables have appropriate indexes on email, status, date_added, and any custom field columns used for segmentation.

# MySQL optimization for large MailWizz installations
# Check slow query log:
SHOW VARIABLES LIKE 'slow_query_log%';
SET GLOBAL slow_query_log = 'ON';
SET GLOBAL long_query_time = 1;  # Log queries over 1 second

# Key indexes to verify exist:
SHOW INDEX FROM mailwizz_lists_subscribers;
# Should have indexes on: email, status, date_added, list_id

# Add missing index if needed:
ALTER TABLE mailwizz_lists_subscribers 
  ADD INDEX idx_email_status (email, status);
  
# Campaign sends table — index on campaign_id + subscriber_id:
ALTER TABLE mailwizz_campaigns_tracking_opens
  ADD INDEX idx_campaign_sub (campaign_id, subscriber_id);

Security Considerations

MailWizz installations handling production sending volumes are valuable targets. Key security practices: use HTTPS for all MailWizz access (including tracking and unsubscribe links), restrict Backend access to authorized IP ranges via web server configuration, rotate API keys periodically and revoke unused keys, maintain regular database backups (automated, offsite), and ensure PHP and MailWizz are kept current with security patches.

The tracking domain (used for open and click tracking) requires special attention: it must have a valid SSL certificate (Let's Encrypt is acceptable), and its DNS records must point exclusively to your MailWizz server. A compromised tracking domain can redirect recipients to malicious sites or reveal subscriber click data to third parties.

Campaign Analytics Integration

Track this MailWizz configuration area through two complementary metric layers: MailWizz campaign statistics (open rate, click rate, bounce rate, unsubscribe rate) and PowerMTA accounting log data (ISP-specific deferral rate, bounce classification, queue depth). Gaps between the two layers reveal delivery problems invisible to MailWizz statistics alone — high MailWizz "sent" counts with elevated PowerMTA deferral rates indicate a queue buildup that campaign dashboards don't surface.

Review campaign metrics against your own historical baselines rather than industry benchmarks. Your list composition, acquisition source, and engagement history define what normal looks like for your environment. Use rolling 7-day and 30-day averages to distinguish trend changes from campaign-specific variance.

Implementation Checklist

Before deploying this configuration to production MailWizz, verify: delivery server connection test passes in Backend → Servers → Delivery Servers, cron jobs are running on the correct schedule, bounce server mailbox is accessible and IMAP credentials are valid, tracking domain has valid SSL and loads within 500ms, and PHP memory limit is set to at least 256MB.

After deploying, send a test campaign to a controlled list of seed addresses across Gmail, Outlook, and Yahoo. Verify Authentication-Results headers show dkim=pass and spf=pass in the received messages. Check that open and click tracking are registering correctly in MailWizz statistics. Confirm bounce processing is updating subscriber status within 15 minutes of a test bounce event.

For managed MailWizz environments operated by Cloud Server for Email, these verification steps are performed automatically after any configuration change. The managed service includes continuous monitoring of delivery server health, cron job execution, and tracking domain availability. Contact infrastructure@cloudserverforemail.com for information about managed MailWizz hosting.

Chunk Size Optimization for Your Server

The subscriber_chunk_size setting controls how many subscribers are loaded per processing batch during campaign sending. Default (300) is conservative. The optimal setting for your server is the chunk size where memory stays below 80% of limit during peak campaign sends. Test incrementally: set to 500, monitor memory during a live campaign, then try 750. The memory usage grows linearly with chunk size.

Chunk Size vs Database Load

Larger chunk sizes reduce database query frequency (fewer SELECT batches for the same campaign) but increase per-query data volume. At very large chunk sizes (1000+), individual queries can become slow if not properly indexed. Find the balance between memory efficiency (larger chunks) and database query performance (smaller chunks) by monitoring both metrics simultaneously.

Need managed MailWizz infrastructure? We operate fully managed MailWizz and PowerMTA environments for high-volume senders.