The system is running. ProxySQL in front of Aurora, query rules routing reads to replicas, the two-node cluster syncing config in ~600ms, TLS on the backend leg. Four parts to reach this point. The question shifts now: how do you know it’s still working correctly at 3am on a Sunday — and when it isn’t, where do you look?

This part covers the operational layer. The same Lima lab topology from Parts 1–4 runs throughout: dbdeployer MySQL 8.0.41 sandbox (master on port 25001, two replicas on 25002 and 25003), ProxySQL 2.7.3 on proxysql-1 and proxysql-2, backends in HG 10 (writer) and HG 20 (readers). No AWS resources — Part 5 is fully local. The Aurora-specific captures referenced in Section 2 are reused from Parts 2 and 4, cited explicitly.

The System from Parts 1–4 — and What Part 5 Adds

Part 1 made the placement decision. Part 2 wired ProxySQL to Aurora’s native topology discovery — mysql_aws_aurora_hostgroups, REPLICA_HOST_STATUS, 2 errors across 1,485 queries through a live failover. Part 3 built the query routing layer: mysql_query_rules, the ordering rule for SELECT ... FOR UPDATE, transaction_persistent, and the exact conditions that break multiplexing. Part 4 tested the full HA stack under pressure — Aurora at T0+15s, RDS Multi-AZ at T0+64s, TLS footguns in auto-discovery, and NLB health check timing that says 90 seconds in the docs but measured 110 in the lab.

Part 5 adds three things those parts explicitly deferred: the monitoring layer you query to know the system is healthy, the tuning decisions now grounded in observed behavior, and the recovery path for the most common non-obvious production failure mode.

The Monitoring Layer: What to Watch and Why

Three tables cover health at different granularities. Together they answer: is my proxy routing correctly, how loaded is my backend pool, and did my Aurora topology discovery run cleanly?

stats_mysql_query_digest: Workload Shape and Latency Baselines

-- Tested on Lima VMs, MySQL 8.0.41 via dbdeployer, ProxySQL 2.7.3, 2026-05-10

stats_mysql_query_digest accumulates per-digest statistics for every query ProxySQL routes. The primary uses are identifying slow queries by total time spent and reading the shape of your workload — which hostgroups are receiving traffic and in what proportions.

The two captures below show the same ProxySQL instance under two workload patterns. Both are correct behavior. The point is that monitoring tells you which shape you have; your job is to verify it matches what you expect.

Shape A — Transactional workload with transaction_persistent=1 (Part 5 lab, 20 sysbench threads, 300s oltp_read_write):

-- stats_mysql_query_digest on proxysql-1 — top 5 by sum_time, 300s oltp_read_write load
-- Note: all queries land in HG 10 — see analysis below
hostgroup  schemaname  digest_text                                count_star  avg_time_us
10         lab_test    SELECT c FROM sbtest1 WHERE id=?           193270      1936
10         lab_test    SELECT c FROM sbtest2 WHERE id=?           193150      1929
10         lab_test    SELECT c FROM sbtest4 WHERE id=?           191770      1940
10         lab_test    SELECT c FROM sbtest3 WHERE id=?           192110      1927
10         lab_test    COMMIT                                      77046      4280

Every query — including the SELECT statements — landed in HG 10 (the writer). HG 20 (readers) received 644 total executions against 247,593 for HG 10. This is not a routing misconfiguration. The oltp_read_write workload wraps every statement inside BEGIN … COMMIT. With transaction_persistent=1 set on the app user, ProxySQL pins all queries in a detected transaction to the same hostgroup — the writer, where the transaction opened. The reads never had an opportunity to fan out to replicas because the transaction boundary kept them anchored.

The corresponding connection pool snapshot confirms this:

-- stats_mysql_connection_pool on proxysql-1 (mid-load, 20 sysbench threads)
hostgroup  srv_host       srv_port  status  ConnUsed  ConnFree  ConnOK  Latency_us
10         192.168.105.6  25001     ONLINE  20        0         20      2489
20         192.168.105.6  25002     ONLINE   0        12        12      2556
20         192.168.105.6  25003     ONLINE   0         9         9      2445

ConnUsed=20 on HG 10 — one backend connection per sysbench thread, held for the duration of each active transaction. ConnUsed=0 on both replicas — they’re healthy and connected, but receiving no queries.

Shape B — Idle multiplexing baseline (from the Part 3 lab, 100 Python threads, SELECT 1, no session state — reused for comparison):

-- stats_mysql_connection_pool on proxysql-1 (T0+10s, 100 idle frontends, no session state)
-- Source: Part 3 A.5 capture — reused for baseline comparison
hostgroup  srv_host       srv_port  ConnUsed  ConnFree
10         192.168.105.6  25001     0         100
20         192.168.105.6  25002     0          42
20         192.168.105.6  25003     0          61

One hundred frontend sessions open. Zero backend connections pinned to any of them. After SELECT 1 completed, ProxySQL returned all connections to the free pool — the frontends are connected, they just don’t hold a MySQL thread on the other side.

The diagnostic question these two shapes answer: if you expect reads to distribute across replicas but HG 20 shows ConnUsed=0 and stats_mysql_query_digest shows all executions in HG 10, check transaction_persistent first, then check whether your ORM or application wraps reads inside explicit transactions. Both shapes above represent correct behavior for their respective workloads. The monitoring tells you which one you’re looking at.

The two queries to run routinely:

-- Top 10 queries by total time spent — identifies slow-query candidates
SELECT hostgroup, schemaname, username, digest_text,
       count_star,
       ROUND(sum_time / count_star) AS avg_time_us,
       max_time
FROM stats_mysql_query_digest
ORDER BY sum_time DESC
LIMIT 10;

-- Per-hostgroup execution distribution — reveals workload shape
SELECT hostgroup,
       COUNT(DISTINCT digest)    AS unique_queries,
       SUM(count_star)           AS total_executions
FROM stats_mysql_query_digest
GROUP BY hostgroup
ORDER BY hostgroup;

Rising avg_time_us on a digest that was previously stable is the early slow-query signal. Unexpected hostgroup skew — all traffic in HG 10 when you expect a 70/30 read split — tells you to check transaction_persistent or your query rules before blaming the backends.

mysql_server_aws_aurora_log: Topology Detection and Gap Alerting

monitor.mysql_server_aws_aurora_log is the only table that shows Aurora topology discovery in real time. ProxySQL writes a row every check_interval_ms for each backend it polls. It’s the authoritative record of whether ProxySQL is successfully reading INFORMATION_SCHEMA.REPLICA_HOST_STATUS — and how long each poll took.

-- monitor.mysql_server_aws_aurora_log — healthy polling pattern (3-row excerpt)
-- Source: Part 2 live Aurora lab capture, 2026-05-08 (reused — no AWS resources in Part 5)
SELECT check_utc, hostname, is_writer_per_replica_host_status AS writer_detected, lag_ms
FROM monitor.mysql_server_aws_aurora_log
ORDER BY check_utc DESC
LIMIT 3;

check_utc               hostname (polled)                           writer_detected              lag_ms
2026-05-08 10:53:16     proxysql-aurora-EXAMPLE-writer.EXAMPLE...  proxysql-aurora-EXAMPLE-writer   0
2026-05-08 10:53:14     proxysql-aurora-EXAMPLE-reader.EXAMPLE...  proxysql-aurora-EXAMPLE-writer   0
2026-05-08 10:53:12     proxysql-aurora-EXAMPLE-reader.EXAMPLE...  proxysql-aurora-EXAMPLE-writer   0

A healthy pattern: rows appear at roughly check_interval_ms intervals, writer_detected is consistent across all rows in a given window, and lag_ms stays low or zero. The 6-second detection gap from the Part 2 and Part 4 failover captures appeared in this table exactly: ProxySQL polled on schedule throughout, but Aurora’s backends were unreachable mid-promotion, so no rows appear between 10:53:10 and 10:53:16. Section 3 covers how to size check_interval_ms against that observed promotion floor.

Detection gap alerting rule: alert when no successful poll row appears for more than 2×check_interval_ms. At check_interval_ms=2000, that’s a 4-second silence. Any gap longer than that means ProxySQL either can’t reach the backend or Aurora’s control plane is mid-promotion. This is the right threshold to wire into your monitoring system — not a static time value, but a function of your configured polling interval.

stats_mysql_connection_pool and stats_mysql_processlist: Pool Headroom

stats_mysql_connection_pool answers the connection budget question: how close am I to exhausting the backend pool? The ConnUsed / (ConnUsed + ConnFree) ratio is the headroom metric. The mid-load capture from the Part 5 lab shows the pattern for a transactional workload:

-- stats_mysql_connection_pool on proxysql-1 (mid-load, 20 threads, oltp_read_write)
-- Tested on Lima VMs, MySQL 8.0.41 via dbdeployer, ProxySQL 2.7.3, 2026-05-10
hostgroup  srv_host       srv_port  status  ConnUsed  ConnFree  ConnOK  ConnERR  Queries   Latency_us
10         192.168.105.6  25001     ONLINE  20        0         20      0        181493    2489
20         192.168.105.6  25002     ONLINE   0        12        12      2280     116       2556
20         192.168.105.6  25003     ONLINE   0         9         9      2230     83        2445

A few readings worth calling out. ConnERR of 2,280 and 2,230 on the replicas are artifacts of earlier lab sessions, not live failures — verify by watching whether they increment during active load. If ConnERR climbs alongside a ConnUsed spike, that’s a backend connectivity problem. If it’s static, it’s historical noise.

Latency_us is the proxy-measured round-trip for health checks to each backend. Rising latency on one backend before rising ConnERR is the early warning signal: the backend is struggling before it starts failing checks. At 2,489µs on the master and ~2,500µs on the replicas, latency is healthy and symmetric in this capture.

Pool headroom alert threshold: flag when ConnUsed / (ConnUsed + ConnFree) > 0.8 sustained for more than 30 seconds on any hostgroup. Below 0.5 at steady state is healthy. Above 0.8 means you’re approaching the connection ceiling — either raise max_connections in mysql_servers, add a backend, or reduce transaction_persistent scope if the workload allows it.

stats_mysql_processlist gives the live per-session view — which hostgroup each frontend session is currently assigned to and what command it’s running:

-- stats_mysql_processlist on proxysql-1 (mid-load snapshot)
SELECT SessionID, user, db, hostgroup, command, time_ms, info
FROM stats_mysql_processlist
ORDER BY time_ms DESC
LIMIT 10;

During the Part 5 sustained load, all 20 sessions showed hostgroup=10 with a mix of Execute and Sleep states. A session in Sleep with time_ms climbing means it’s holding an open backend connection without issuing queries — the cost of transaction_persistent=1 in a slow-consumer application. Use processlist during incidents to see exactly which sessions are holding pool resources and which queries are actively executing.

Production Sizing: Polling Intervals, Lag Thresholds, Multiplexing

The right values for these variables don’t come from the ProxySQL docs. They come from your own observed promotion time and workload shape. Here’s how to derive them from the data Parts 2–4 already captured.

check_interval_ms: Sizing Against the Promotion Floor

The detection latency formula from Part 2’s detection math section:

detection latency = Aurora internal promotion time (~6s, opaque to ProxySQL)
                  + at most one check_interval_ms cycle

Aurora’s internal promotion time is the floor — ProxySQL was polling on schedule throughout both the Part 2 and Part 4 failovers, but the backends were simply unreachable while Aurora was mid-promotion. Lowering check_interval_ms below 1000ms adds polling load on Aurora’s INFORMATION_SCHEMA without meaningfully reducing detection latency — the floor is Aurora’s promotion time, and that’s set by instance class and cross-AZ replication state, not polling frequency.

check_interval_ms controls the worst-case additional lag on top of that floor:

The Part 2 lab used check_interval_ms=2000; Part 4 used 5000. Both labs produced identical detection floors because the constraint was Aurora’s ~6-second internal promotion, not polling frequency. Choose based on the detection window your application’s connection pool and retry logic can tolerate — not on the assumption that faster polling reduces the floor. check_timeout_ms must also remain below check_interval_ms and at or below 3000ms (ProxySQL 2.7.3 enforces this with a CHECK constraint; a silent INSERT failure is the symptom if you exceed it, as documented in Part 4).

Lag Thresholds: max_replication_lag vs max_lag_ms

These are two different columns in two different tables with different units. Conflating them produces a config that looks correct but either does nothing or clips reads far more aggressively than intended.

The footgun: max_lag_ms=600000 in mysql_aws_aurora_hostgroups means 600000 milliseconds (= 600 s = 10 minutes of acceptable Aurora replica lag) — a generous lab default from Part 2; the column name carries the _ms unit. The sibling knob mysql_servers.max_replication_lag is in whole seconds for standard replication lag. Copying the numeric literal 600000 from max_lag_ms into max_replication_lag does not mean “10 minutes”; it means 600000 seconds (about 7 days). Your replicas would have to lag roughly a week before ProxySQL excluded them from routing.

Lab result for max_replication_lag: with max_replication_lag=2 set on replica2 (port 25003) and the replica’s SQL thread stopped, Seconds_Behind_Source returns NULL. ProxySQL treats NULL as 60 seconds of lag by default — so a stopped SQL thread suddenly looks like a 60-second-lagging replica even though the underlying data is fine. The variable mysql-monitor_slave_lag_when_null=60 controls this; size it based on how tolerant your application is of reads from a replica whose SQL thread is stopped.

With slave_lag_when_null=60 and max_replication_lag=2, replica2 transitioned to SHUNNED within one monitor_replication_lag_interval cycle (10 seconds) after the SQL thread was stopped. The status was SHUNNED, not OFFLINE_SOFT — that’s the actual ProxySQL 2.7.3 behavior for lag-threshold violations. Reads stopped routing to replica2 immediately; replica1 absorbed them cleanly.

Recovery after START REPLICA SQL_THREAD: replica2 returned to ONLINE in approximately 72 seconds — about 7× the 10-second monitor_replication_lag_interval, as the lag counter drained across multiple polling cycles before ProxySQL confirmed it was clear. Recovery time is bounded by monitor_replication_lag_interval × monitor_replication_lag_count polling cycles, not by a fixed timeout.

transaction_persistent and Multiplexing Variables

The Part 5 sysbench capture (all traffic in HG 10) makes the transaction_persistent tradeoff concrete. With transaction_persistent=1, queries inside an open transaction stay on the writer. This is correct for application accounts that hold real transactions — the alternative, allowing in-transaction reads to jump to a replica, would route a SELECT to a server that doesn’t yet have the transaction’s uncommitted writes visible, which produces inconsistent reads without any error. Don’t set transaction_persistent=0 for application accounts that use explicit transactions or that issue DML.

Set transaction_persistent=0 for analytics or reporting accounts that connect, run a read, and disconnect — no open transactions, no consistency hazard. This is the same analytics user pattern from Part 3.

Two monitor variables worth knowing for the lag and health check rhythm:

• mysql-monitor_ping_interval=10000 (10s default): how often ProxySQL sends COM_PING to each backend on existing connections. With mysql-monitor_ping_max_failures=3, three consecutive ping failures trigger SHUNNED — a 30-second window of consistently-failing pings before a backend is excluded.
• mysql-wait_timeout=28800000 (8 hours): how long ProxySQL keeps backend connections alive. This means a credential change on the MySQL side doesn’t immediately invalidate existing ProxySQL connections — they continue using the cached auth until the connections cycle out or a new connection attempt fails. Section 5 covers exactly what this looks like when it’s the monitor user whose credentials change.

Rolling Upgrade Runbook

Step 1 — Capture pre-upgrade baseline. Record the version and connection pool state on both nodes before touching anything. If something goes wrong during the upgrade, this snapshot is your reference point.

-- Pre-upgrade version baseline on both nodes (proxysql-1 shown)
-- Tested on Lima VMs, ProxySQL 2.7.3, 2026-05-10
SELECT @@version;
-- 2.7.3-12-g50b7f85

SELECT hostgroup, srv_host, srv_port, status, ConnUsed, ConnFree
FROM stats_mysql_connection_pool
ORDER BY hostgroup, srv_port;

Step 2 — Start background traffic. Run a SELECT loop from your client against both ProxySQL nodes simultaneously. Log every response with a timestamp — this is the evidence trail that quantifies the upgrade’s error window. In production, your application’s existing traffic serves this purpose; in a maintenance window, an explicit probe script gives you a clean record.

Step 3 — Drain proxysql-1. In production: deregister proxysql-1 from the NLB target group first (NLB default connection draining: 30 seconds). Wait for in-flight connections to finish, then stop the service. The NLB routes all new connections to proxysql-2 from the moment the target is deregistered. In the lab, where there’s no NLB, stopping the service directly simulates this:

# Drain proxysql-1 (lab simulation of NLB target deregistration + service stop)
# Production: deregister from NLB first, wait for connection draining, then stop
sudo systemctl stop proxysql

Verify proxysql-1 is unreachable on port 6033 and proxysql-2 is serving normally before proceeding. Part 4’s NLB section covers the 110-second real-world detection window versus the theoretical 90-second threshold — size your drain window accordingly.

Step 4 — Upgrade the binary.

# Upgrade ProxySQL binary (replace 2.7.X with your target version)
sudo apt-get install proxysql=2.7.X

Step 5 — Start the service and verify cluster sync. After systemctl start proxysql, the restarted node bootstraps from its peer automatically — given a populated proxysql_servers table and matching cluster credentials, it fetches the current runtime config from proxysql-2 within the cluster’s check_interval_ms window (~600ms in our lab from Part 4).

# Start ProxySQL after upgrade; cluster sync bootstraps from peer automatically
sudo systemctl start proxysql

-- Verify cluster sync on the restarted node: runtime_mysql_servers should match proxysql-2
SELECT hostgroup_id, hostname, port, status
FROM runtime_mysql_servers
ORDER BY hostgroup_id, port;

If runtime_mysql_servers shows the master (port 25001) in both HG 10 and HG 20 after restart, that’s expected: mysql-monitor_writer_is_also_reader=true places the master in both the writer and reader hostgroups. It’s not a routing anomaly — it reflects the ProxySQL default that allows reads to land on the writer when both replicas are lagging or SHUNNED.

If mysql_servers doesn’t arrive on the restarted node, check whether admin-cluster_mysql_servers_sync_algorithm=1 (delta mode) is set and the node has no sync baseline — the bootstrap footgun from Part 4. Set it to 0 temporarily to force a full pull, then restore 1.

Step 6 — Re-register with NLB (production step) and spot-check traffic through the upgraded node.

Step 7 — Repeat for proxysql-2.

Lab timing for both nodes:

The per-node time includes the apt-get install step. In a real upgrade where the package download is already cached, the binary swap itself takes under 5 seconds — the remaining time is service start, monitor thread initialization, and cluster sync confirmation.

Troubleshooting: When Something Goes Wrong

The most common non-obvious production failure with ProxySQL follows a specific pattern: backends appear SHUNNED or errors start climbing, the instinct is to check Aurora or the MySQL backends directly, but the actual cause lives in a ProxySQL internal table that most DBAs don’t check first. Here’s the diagnostic sequence that surfaces it quickly.

The worked example is monitor user credential revocation. ProxySQL connects to each backend using the monitor user (set via mysql-monitor_username and mysql-monitor_password) to run health checks — COM_PING, SHOW REPLICA STATUS for replication lag, SHOW GLOBAL VARIABLES LIKE 'read_only' for writer detection. If those credentials break — password rotation without updating ProxySQL, a permission change by someone who didn’t know the monitor user was load-bearing — every health check against every backend starts failing simultaneously.

The troubleshooting flowchart:

Symptom: rising errors or SHUNNED backends in runtime_mysql_servers
                │
                ▼
   Step 1: Check runtime_mysql_servers
   ──────────────────────────────────────────────────────
   All ONLINE?
     YES → backend health is fine → check query rules
           and stats_mysql_query_digest for routing anomalies
     NO (SHUNNED present) → continue ↓
   ──────────────────────────────────────────────────────
                │
                ▼
   Step 2: Check mysql_server_connect_log
   ──────────────────────────────────────────────────────
   SELECT hostname, port, time_start_us,
          connect_success_time_us, connect_error
   FROM monitor.mysql_server_connect_log
   ORDER BY time_start_us DESC LIMIT 20;

   connect_error = NULL?
     YES → connect checks are clean → go to ping_log
     "Access denied for user 'monitor'" → FOUND IT
   ──────────────────────────────────────────────────────
                │
                ▼
   Step 3: Confirm with mysql_server_ping_log
   ──────────────────────────────────────────────────────
   SELECT hostname, port, time_start_us,
          ping_success_time_us, ping_error
   FROM monitor.mysql_server_ping_log
   ORDER BY time_start_us DESC LIMIT 20;

   Same "Access denied" pattern? → confirms monitor credentials
   "Gone away" / timeout? → backend connectivity problem
   ──────────────────────────────────────────────────────
                │
                ▼
   Step 4: Verify the monitor user directly on the backend
   ──────────────────────────────────────────────────────
   mysql -h <backend-host> -P <port> -u monitor -p'<pass>' \
     -e "SHOW REPLICA STATUS\G"

   Access denied → confirm which grant is missing
   ──────────────────────────────────────────────────────
                │
                ▼
   Step 5: Restore
   ──────────────────────────────────────────────────────
   On the MySQL backend (run on master; replicated to replicas):
     GRANT REPLICATION CLIENT ON *.* TO 'monitor'@'%';
     GRANT SELECT ON sys.* TO 'monitor'@'%';
     FLUSH PRIVILEGES;

   If the password changed on the MySQL side, also update ProxySQL:
     SET mysql-monitor_password='<new-pass>';
     LOAD MYSQL VARIABLES TO RUNTIME;
     SAVE MYSQL VARIABLES TO DISK;
   ──────────────────────────────────────────────────────

What the lab capture shows. After changing the monitor user’s password to an incorrect value on the MySQL backend, monitor.mysql_server_connect_log filled with this pattern on the next connect-check cycle:

-- monitor.mysql_server_connect_log — credential failure in progress
-- connect_interval=60s; errors appear once per cycle on each backend
hostname       port   time_start_us       connect_success_time_us  connect_error
192.168.105.6  25001  1778361407360863    0   Access denied for user 'monitor'@'proxysql-1' (using password: YES)
192.168.105.6  25002  1778361406680516    0   Access denied for user 'monitor'@'proxysql-1' (using password: YES)
192.168.105.6  25003  1778361408042762    0   Access denied for user 'monitor'@'proxysql-1' (using password: YES)

All three backends, same error, every connect-check cycle. That pattern — not one backend, not an intermittent error, but every backend on every cycle — points directly at the monitor credentials, not at backend connectivity.

Auth failures don’t trigger SHUNNED instantly. The connect check fires every mysql-monitor_connect_interval (default 60s), and ProxySQL needs mysql-monitor_ping_max_failures consecutive ping failures before it formally SHUNs a backend. What you see first is the connect log filling with "Access denied" entries — one per polling cycle per backend. That window, from first error to formal SHUNNED, is your diagnostic opportunity. The signal is clear and early; the cascade is gradual by design. A credential problem that would cause a full SHUNNED state on all backends gives you several minutes of warning in mysql_server_connect_log before client traffic starts seeing widespread errors.

Recovery is equally bounded by the polling cycle. After restoring the correct credentials on the MySQL backend, the connect log showed a clean entry 11 seconds after the GRANT was restored — that’s wherever in the 60-second polling cycle the next connect check happened to fire. The range is 0 to mysql-monitor_connect_interval (60s default); expect recovery on the next monitor poll after the fix is applied.

What’s Not in This Series

Three topics adjacent to this series are real and important. Each deserves its own treatment.

Sharding. ProxySQL supports basic query-level sharding — routing by schema boundary or by a rule that hashes a user ID into a destination hostgroup. For simple cases this works. For production sharding at scale, with consistent cross-shard transactions and managed schema migrations, this is Vitess territory. ProxySQL’s sharding support is a routing primitive, not a sharding framework.

Multi-region Aurora + ProxySQL. Aurora Global Database places a writer in one region and reader clusters in others, with sub-second replication lag. ProxySQL in front of a Global Database deployment is a different configuration: mysql_aws_aurora_hostgroups scoped per-region, topology discovery that stays local while the primary region is healthy, and failover coordination when a secondary region is promoted to writer. This series covers single-region Aurora only.

PostgreSQL ProxySQL HA. ProxySQL speaks MySQL wire protocol. For PostgreSQL, the equivalent stack is different: see the ProxySQL PostgreSQL HA series which covers the same placement-to-operations arc for PostgreSQL backends.

Across Five Parts

Across five parts, you’ve built and operated a production-representative ProxySQL + Aurora MySQL topology: decided where the proxy layer goes and why, wired it to Aurora’s native topology discovery, tuned query routing and multiplexing against real workload patterns, tested HA under a live failover with a measured error count, and now have the monitoring queries and runbooks to operate it day-to-day.

Three things adjacent to this series worth exploring from here: the Aurora Performance Insights layer for correlating ProxySQL digest data with query execution inside the database engine itself; the ProxySQL Prometheus exporter for time-series dashboards that alert on ConnUsed headroom and detection gaps without manual polling; and slow query log parsing to match ProxySQL’s stats_mysql_query_digest patterns against Aurora’s slow log and identify the same queries from both sides of the proxy.

If you’re standing up ProxySQL in front of RDS or Aurora MySQL and want a second pair of eyes before production traffic, book a free assessment.