Checkout is a write path. It’s not a search box. It’s not a cache-friendly catalog browse. It’s a pile of inserts, updates, constraints, and “did we really charge them twice?” paranoia—all arriving in little bursts shaped like marketing campaigns.
When ecommerce writes go sideways, it’s rarely subtle. Latency spikes, replication lags, inventory oversells, and suddenly your “high availability” plan is a group chat and a prayer. The question isn’t “MariaDB or PostgreSQL is faster.” The question is: which one chokes first under your specific write pattern, and what do you do before it happens on a Saturday.
The uncomfortable truth: both can choke
MariaDB and PostgreSQL are both serious databases. They can both deliver frightening throughput and surprisingly civilized latency. They can also both fall apart in very predictable ways when you do ecommerce writes like a stampede: inventory reservations, payment state machines, shipping quotes, coupon usage, and “audit everything forever.”
What differs is how they fail, how much warning you get, and how expensive it is to make the failure mode go away.
- PostgreSQL is usually limited by WAL and checkpoint dynamics, vacuum debt, and transaction ID wraparound risk if you ignore housekeeping. When it’s unhappy, you’ll see IO pressure, bloat, and weird “the database is alive but nothing is moving” lock queues.
- MariaDB (InnoDB) is usually limited by redo/binlog pressure, replication lag dynamics, mutex/lock contention around hot rows, and occasionally “we tuned it to be fast” settings that quietly delete your recovery guarantees. When it’s unhappy, you’ll see replication get behind, deadlocks spike, and commits start queuing like a bad airport security line.
Pick based on your write pattern, your operational maturity, and your willingness to enforce discipline in schema and transaction design. If you can’t enforce discipline, pick the system whose failure mode you can detect and mitigate fastest. Pride is not a monitoring strategy.
Write patterns that break ecommerce
Most ecommerce platforms don’t die from “too many queries.” They die from a few specific write patterns that scale nonlinearly.
1) Hot rows: inventory, coupons, and counters
If you have a single SKU going viral, a single inventory row becomes a contested resource. If you have “coupon uses remaining” in one row, it becomes a contested resource. If you track “orders_today” as a single counter, you created your own DDoS—internally.
Both databases can handle contention, but neither can defy physics: a single row can only be updated so many times per second before you serialize your whole checkout. The fix is rarely “more CPU.” It’s usually changing the data model or isolating the hot path.
2) Long transactions during checkout workflows
Checkout is a workflow. Workflows invite long transactions: reserve inventory, call payment provider, compute tax, talk to shipping API, write audit rows, then commit. That’s a great way to hold locks while waiting on the internet.
If your transaction spans network calls, you’re basically holding your database hostage until someone else’s pager goes off.
3) Secondary indexes everywhere
Every index is a write. Every index also needs maintenance. In write-heavy tables (orders, order_items, payments, inventory_events), index enthusiasm is how you buy latency and IO amplification at full retail price.
4) Heavy “append-only” auditing without partitioning
Audit tables are the new logs. Except logs rotate, and audit tables often don’t. Write-heavy append-only tables without partitioning invite bloat, giant indexes, slow deletes, and vacuum stress (Postgres) or purge pressure (InnoDB).
5) Replication and durability turned into a performance hack
Eventually someone says: “We can make it faster if we relax durability.” They aren’t wrong; they’re just gambling with business state. You can relax durability safely in some places. Checkout isn’t one of them.
Joke #1: Turning off durability for faster checkout is like removing seatbelts because you’re late—your ETA improves right up until it doesn’t.
How PostgreSQL usually chokes first
Postgres write mechanics in one paragraph (the version you need at 2 a.m.)
Postgres uses MVCC with tuple versions: updates create new row versions, old versions stick around until vacuum reclaims them. Every transaction writes WAL for durability and replication. Checkpoints flush dirty pages. If autovacuum falls behind, you get bloat and rising IO. If checkpoints are too aggressive or WAL is constrained, you get IO spikes and commit latency. If you ignore transaction ID maintenance, you can approach wraparound and be forced into emergency vacuuming at the worst moment.
Failure mode A: WAL and checkpoint pressure → latency spikes
Under heavy write load, the WAL stream becomes the heartbeat. If WAL can’t be written fast enough (slow disk, saturated IOPS, bad fsync behavior), commits queue. Then checkpoints arrive and start flushing a lot of dirty buffers, fighting with foreground writes. The symptom is usually p99 latency spikes and IO utilization pegged, not necessarily CPU.
Operationally, Postgres tends to “tell on itself” through metrics: checkpoint frequency, buffers written by backends, WAL write times. If you look.
Failure mode B: vacuum debt → bloat → everything gets slower
MVCC is great until you forget to clean up. If your write-heavy tables are constantly updated and your autovacuum can’t keep up, you get bloat. Bloat means larger indexes, larger heap, more cache misses, more IO. The database is still doing the same logical work, but now it’s dragging a larger suitcase everywhere.
On ecommerce, the usual bloat generators are: “order state” updates, inventory state updates, payment retries, and any “upsert” pattern that turns into an update storm.
Failure mode C: lock queues that look like a slow database
Postgres is good at row-level locking, but long transactions, DDL in production, and misguided “SELECT … FOR UPDATE” patterns can cause lock chains. You’ll hear: “Postgres is slow today.” It isn’t slow; it’s blocked.
Failure mode D: too many connections (and the illusion of scaling)
Postgres uses a process (or process-like) model; too many connections create memory overhead and context switching. A write-heavy system with 2,000 active connections can look like a CPU problem but is often a connection management problem. The fix is boring: pooling, sane timeouts, and backpressure.
How MariaDB usually chokes first
InnoDB write mechanics in one paragraph (the “why are commits stuck?” version)
InnoDB uses redo logs and a buffer pool. Writes go to memory and redo; pages flush later. Binary logs (binlog) record changes for replication. Commit can involve flushing redo and/or binlog depending on durability settings and group commit behavior. Under load, the system is often limited by redo log write bandwidth, flush behavior, or replication applying changes downstream.
Failure mode A: replication lag becomes the real outage
Many ecommerce setups lean on replicas for reads. Under write-heavy load, replicas can lag. Then your application reads stale state: inventory looks available when it isn’t, payment state looks “pending” when it’s “captured,” and customers get duplicate confirmation emails. The database might be healthy; the architecture is not.
MariaDB replication lag is often amplified by: large transactions, single-threaded apply (depending on configuration), and schema designs that force contention on the replica as it replays writes.
Failure mode B: hot-row contention and deadlocks
InnoDB is generally excellent, but hot rows (inventory, counters, coupon rows) create lock contention. With enough concurrency, you get deadlocks. Deadlocks are not inherently bad—InnoDB detects them and rolls back one transaction—but repeated deadlocks can turn into a throughput ceiling and a thundering herd of retries.
Failure mode C: fsync and flush settings that are fast until you crash
MariaDB makes it easy to trade durability for performance (and people do). Settings like relaxed redo flush or binlog sync reduce fsyncs and increase throughput. Then a node crashes and you discover your “small risk” is a cartful of missing orders.
Failure mode D: secondary index write amplification and page splits
Write-heavy tables with multiple secondary indexes can cause heavy random IO. If the primary key is poorly chosen (e.g., random UUID without an ordering strategy), you can amplify page splits and churn the buffer pool. This is not unique to MariaDB, but InnoDB’s clustered index makes primary key choice especially consequential.
Decision guide: what to pick for write-heavy checkout
If your write pattern is “many updates to the same rows”
This is the hot-row problem. Both systems suffer. Your best move is data model surgery:
- Use append-only events for inventory movements and compute availability with a derived view (or a cached projection) instead of constantly updating one row.
- Shard counters (per-minute/per-bucket) instead of a single global counter.
- Use idempotency keys so retries don’t multiply writes.
Database choice helps less than schema and transaction design here.
If your write pattern is “append-only, high volume”
Postgres is often easier to keep stable if you invest in partitioning and autovacuum tuning; it gives you strong introspection. MariaDB can do very well too, but you must watch redo/binlog pressure and replica apply speed like a hawk.
If you rely heavily on read replicas for correctness-adjacent reads
Be careful with MariaDB asynchronous replication unless you design for it. Postgres async replication also lags, but Postgres shops often pair it with clearer patterns: read-your-writes on primary for critical flows, and a pooler plus routing logic. Either way: if a read affects a write decision (inventory remaining, coupon validity), read from a source with the right consistency.
If you want one line: who chokes first?
- PostgreSQL tends to choke first on IO and housekeeping debt: WAL throughput, checkpoint flush spikes, vacuum falling behind, connection overload.
- MariaDB tends to choke first on commit/replication dynamics and contention: redo/binlog flush behavior, replica lag, hot-row lock contention, deadlock storms.
That’s not a verdict; it’s a heads-up. You can prevent both. But you can’t prevent what you don’t measure.
Interesting facts and historical context (useful, not trivia)
- PostgreSQL’s MVCC lineage traces back to academic work in the 1980s; its multi-version design is why reads don’t block writes in the classic way.
- InnoDB wasn’t originally “the MySQL engine”; it became the default later because it fixed the “oops, table-level locks” era that early MySQL users remember too well.
- MariaDB was created as a fork after concerns about MySQL’s stewardship; many operational behaviors remain MySQL-shaped, including replication conventions.
- Postgres replication matured later than MySQL-style binlog replication; modern streaming replication is strong, but the ecosystem still pushes best practices like connection pooling more aggressively.
- Postgres updates are not in-place; each update creates a new tuple version. That’s a feature, and it’s also why bloat is a thing you must budget for in write-heavy systems.
- InnoDB’s clustered primary key means table data is physically organized by primary key. Primary key choice can strongly influence write amplification and page splits.
- Postgres has “HOT updates” (heap-only tuple updates) that can avoid index updates in some cases, which can materially reduce write IO for certain update patterns.
- Both systems can do “async commit” and other durability tradeoffs. The trap is treating those knobs as performance features instead of risk multipliers.
- Large transactions hurt replication disproportionately in both ecosystems: they delay visibility on replicas and make crash recovery/rewind more painful.
Fast diagnosis playbook
When checkout write latency spikes, you have minutes to decide: is this CPU, IO, locks, replication, or application-side connection pileup? Here’s the order that finds the bottleneck fastest in practice.
First: confirm the symptom is real and scoped
- Is p95/p99 commit latency up, or just a subset of endpoints?
- Is it all writes, or only one table (inventory/orders/payments)?
- Is the primary impacted or only replicas?
Second: check locks and waiting, not “slow queries”
Write outages are often lock queues disguised as slowness. Find the blocked sessions, find the blocker, and decide whether to kill a transaction or fix the pattern.
Third: check IO saturation and fsync behavior
If storage is pegged, the database can’t commit quickly. Check device utilization, await times, and WAL/binlog write rates. If you’re on networked storage, assume it’s guilty until proven innocent.
Fourth: replication health and lag
Lag isn’t just a reporting problem; it can break correctness if you read from replicas. If lag is climbing during the incident, stop routing critical reads to replicas immediately.
Fifth: housekeeping debt
In Postgres: autovacuum, bloat, checkpoint churn. In MariaDB: purge lag, history list length, buffer pool pressure. Housekeeping is where “it was fine yesterday” goes to die.
Paraphrased idea from Werner Vogels (reliability/ops): “Everything fails; design so you can recover quickly.” That’s the posture you want during write incidents—fast containment beats perfect diagnosis.
Hands-on tasks: commands, outputs, and decisions
These are real operational tasks you can run during an incident or a performance review. Each includes: command, what the output means, and what you decide next. Commands assume Linux and local access to the DB host or a jump host with the right clients.
Task 1: Is the box IO-bound right now?
cr0x@server:~$ iostat -x 1 5
Linux 6.1.0 (db-primary) 12/29/2025 _x86_64_ (16 CPU)
avg-cpu: %user %nice %system %iowait %steal %idle
12.44 0.00 6.21 22.35 0.00 59.00
Device r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await svctm %util
nvme0n1 120.0 950.0 4800.0 76000.0 150.2 9.80 10.3 0.7 78.0
dm-0 118.0 940.0 4700.0 75500.0 150.6 14.10 15.5 0.8 92.0
Meaning: High %iowait and device %util near saturation with elevated await suggests IO is the limiter; commits will queue.
Decision: Reduce write pressure (feature flags, rate limits), ensure WAL/binlog devices aren’t contending with data, and consider increasing checkpoint/WAL settings (Postgres) or redo/binlog durability group commit behavior (MariaDB) after the incident.
Task 2: Which process is doing IO damage?
cr0x@server:~$ pidstat -d 1 5
Linux 6.1.0 (db-primary) 12/29/2025 _x86_64_ (16 CPU)
# Time UID PID kB_rd/s kB_wr/s kB_ccwr/s Command
12:01:01 PM 999 21450 50.00 68000.00 0.00 postgres
12:01:01 PM 999 21990 0.00 4200.00 0.00 postgres
12:01:01 PM 999 18210 0.00 39000.00 0.00 mariadbd
Meaning: You can see whether the DB daemon is writing heavily. If you see multiple DB daemons writing, you might be on the wrong host or running both.
Decision: If writes are dominated by the DB, go inside the DB for lock/WAL insight. If not, find the real writer (backup, logrotate, runaway batch job).
Task 3 (Postgres): Are we waiting on locks or IO?
cr0x@server:~$ psql -X -c "SELECT wait_event_type, wait_event, count(*) FROM pg_stat_activity WHERE state <> 'idle' GROUP BY 1,2 ORDER BY 3 DESC;"
wait_event_type | wait_event | count
----------------+---------------------+-------
Lock | transactionid | 23
IO | WALWrite | 7
LWLock | BufferMapping | 4
| | 2
Meaning: Many sessions waiting on Lock/transactionid indicates contention or long transactions. IO/WALWrite points to WAL write bottlenecks.
Decision: If lock waits dominate, identify blockers and shorten transactions. If WALWrite dominates, focus on storage/WAL settings and checkpoint behavior.
Task 4 (Postgres): Who is blocking whom?
cr0x@server:~$ psql -X -c "SELECT a.pid AS blocked_pid, a.usename, a.query AS blocked_query, pg_blocking_pids(a.pid) AS blocking_pids FROM pg_stat_activity a WHERE cardinality(pg_blocking_pids(a.pid)) > 0;"
blocked_pid | usename | blocked_query | blocking_pids
------------+---------+--------------------------------------+--------------
18722 | app | UPDATE inventory SET reserved = ... | {18410}
18740 | app | UPDATE inventory SET reserved = ... | {18410}
Meaning: One blocker PID holding a lock is stalling many checkout writes—classic hot-row or long transaction.
Decision: Inspect the blocker session; if it’s safe, cancel it. Then fix the application pattern (reduce lock scope, avoid network calls inside txn, redesign inventory).
Task 5 (Postgres): Are checkpoints thrashing?
cr0x@server:~$ psql -X -c "SELECT checkpoints_timed, checkpoints_req, buffers_checkpoint, buffers_backend, checkpoint_write_time, checkpoint_sync_time FROM pg_stat_bgwriter;"
checkpoints_timed | checkpoints_req | buffers_checkpoint | buffers_backend | checkpoint_write_time | checkpoint_sync_time
------------------+-----------------+-------------------+-----------------+-----------------------+----------------------
122 | 480 | 9823412 | 2210344 | 8931201 | 1203310
Meaning: High checkpoints_req relative to timed suggests WAL pressure is forcing checkpoints. High buffers_backend suggests backends are doing their own flushing—often bad for latency.
Decision: After containment, tune max_wal_size, checkpoint_timeout, and checkpoint_completion_target, and validate storage throughput for WAL and data.
Task 6 (Postgres): Is autovacuum keeping up on hot tables?
cr0x@server:~$ psql -X -c "SELECT relname, n_dead_tup, n_live_tup, last_autovacuum FROM pg_stat_user_tables ORDER BY n_dead_tup DESC LIMIT 10;"
relname | n_dead_tup | n_live_tup | last_autovacuum
--------------+------------+------------+--------------------------
orders | 18422012 | 22100410 | 2025-12-29 11:02:14+00
payments | 9221011 | 8012200 | 2025-12-29 10:41:02+00
inventory | 5120099 | 210440 | 2025-12-29 07:12:55+00
Meaning: Lots of dead tuples relative to live tuples suggests vacuum is behind or your update pattern is aggressive. Inventory having more dead than live is a tell.
Decision: Tune per-table autovacuum thresholds, reduce update churn, or move to append-only events. Consider partitioning for high-churn tables.
Task 7 (Postgres): Are we drowning in connections?
cr0x@server:~$ psql -X -c "SELECT state, count(*) FROM pg_stat_activity GROUP BY 1 ORDER BY 2 DESC;"
state | count
---------------------+-------
idle | 820
active | 160
idle in transaction | 44
Meaning: Many idle can be okay, but idle in transaction is dangerous: it holds snapshots/locks and blocks vacuum.
Decision: Enforce application-side timeouts, kill “idle in transaction” sessions, and use a pooler. Also fix code paths leaving transactions open.
Task 8 (MariaDB): Are we blocked or deadlocking?
cr0x@server:~$ mysql -e "SHOW ENGINE INNODB STATUS\G" | sed -n '1,120p'
...
LATEST DETECTED DEADLOCK
------------------------
*** (1) TRANSACTION:
TRANSACTION 98234123, ACTIVE 0 sec starting index read
mysql tables in use 1, locked 1
LOCK WAIT 5 lock struct(s), heap size 1136, 3 row lock(s)
MySQL thread id 18220, OS thread handle 140201, query id 921103 app updating
UPDATE inventory SET reserved=reserved+1 WHERE sku_id=771 AND warehouse_id=3;
*** (2) TRANSACTION:
TRANSACTION 98234124, ACTIVE 0 sec fetching rows
...
Meaning: You have real deadlocks on hot rows (inventory). Deadlocks will cause retries; retries amplify load.
Decision: Reduce concurrency for the hot SKU (queue/reservation service), ensure consistent locking order across code paths, and add idempotency to reduce retry storms.
Task 9 (MariaDB): Is replication lag the real fire?
cr0x@server:~$ mysql -e "SHOW SLAVE STATUS\G" | egrep "Seconds_Behind_Master|Slave_IO_Running|Slave_SQL_Running|Last_SQL_Error"
Slave_IO_Running: Yes
Slave_SQL_Running: Yes
Seconds_Behind_Master: 148
Last_SQL_Error:
Meaning: Replica is 148 seconds behind. Reads from it are stale; if the app uses it for inventory/coupon checks, you’ll sell ghosts.
Decision: Route correctness-critical reads to primary; degrade noncritical reads to cache; investigate apply throughput and long transactions on primary.
Task 10 (MariaDB): Are we committing safely, or “fast and loose”?
cr0x@server:~$ mysql -e "SHOW VARIABLES WHERE Variable_name IN ('innodb_flush_log_at_trx_commit','sync_binlog');"
+------------------------------+-------+
| Variable_name | Value |
+------------------------------+-------+
| innodb_flush_log_at_trx_commit | 2 |
| sync_binlog | 0 |
+------------------------------+-------+
Meaning: innodb_flush_log_at_trx_commit=2 and sync_binlog=0 are classic throughput boosters that reduce durability guarantees.
Decision: For checkout, move toward 1/1 (or a consciously chosen compromise). If you can’t afford that, you need architectural changes (batching, eventing, or different storage) rather than pretending physics will negotiate.
Task 11 (Postgres): Is WAL generation exploding?
cr0x@server:~$ psql -X -c "SELECT now() as ts, pg_current_wal_lsn();"
ts | pg_current_wal_lsn
-----------------------------+--------------------
2025-12-29 12:05:13.112+00 | 3A/9F2C1B80
Meaning: One sample isn’t enough; you compare LSNs over time. If it jumps fast, you’re generating WAL rapidly—often from updates, index churn, or a bulk job.
Decision: Identify the highest WAL-generating tables (via stats/extension tooling in your environment), pause bulk writes, or reduce index maintenance (drop unused indexes after validation).
Task 12 (Linux): Is the filesystem lying about flush latency?
cr0x@server:~$ sudo blktrace -d /dev/nvme0n1 -w 3 -o - | blkparse -i -
8,0 1 1 0.000000000 21450 Q WS 0 + 8 [postgres]
8,0 1 2 0.000210000 21450 G WS 0 + 8 [postgres]
8,0 1 3 0.004900000 21450 C WS 0 + 8 [0]
Meaning: You’re seeing write sync completions; if completion (C) timestamps drift high, storage flush latency is hurting commits.
Decision: Validate storage class, firmware, RAID/cache policies. For cloud disks, consider provisioned IOPS or local NVMe for WAL/redo. If you can’t improve storage, reduce fsync frequency only where correctness allows (not checkout).
Task 13 (Postgres): Find “idle in transaction” offenders with query text
cr0x@server:~$ psql -X -c "SELECT pid, usename, now()-xact_start AS xact_age, state, left(query,120) AS q FROM pg_stat_activity WHERE state='idle in transaction' ORDER BY xact_age DESC LIMIT 10;"
pid | usename | xact_age | state | q
-------+---------+-------------+----------------------+------------------------------------------------------------
19221 | app | 00:12:14.22 | idle in transaction | SELECT * FROM carts WHERE cart_id=$1 FOR UPDATE;
Meaning: Somebody started a transaction, grabbed a lock, then went idle. This blocks vacuum and can block writers.
Decision: Kill the session during incident. Then fix app code: transaction scope, timeouts, and avoid locking reads unless you truly need them.
Task 14 (MariaDB): Check InnoDB history list length and purge health
cr0x@server:~$ mysql -e "SHOW ENGINE INNODB STATUS\G" | egrep -i "History list length|Log sequence number|Last checkpoint"
History list length 923441
Log sequence number 1123341234412
Last checkpoint at 1123340031129
Meaning: Large history list length can indicate purge lag, often due to long transactions. That increases undo retention and can degrade performance.
Decision: Find and eliminate long-running transactions, especially on replicas or analytics sessions. Consider separating OLTP from reporting.
Task 15 (Both): Are we accidentally doing huge transactions?
cr0x@server:~$ sudo journalctl -u checkout-worker -n 50 --no-pager
Dec 29 12:06:01 app-1 checkout-worker[3112]: bulk_reservation job started: cart_id=... items=420
Dec 29 12:06:02 app-1 checkout-worker[3112]: db transaction open for cart_id=...
Dec 29 12:06:55 app-1 checkout-worker[3112]: db transaction commit cart_id=... duration_ms=52981
Meaning: A 52-second transaction is not “a bit slow.” It’s a lock/undo/WAL factory. It will hurt both Postgres and MariaDB.
Decision: Break up work, move external calls outside transactions, and design idempotent steps so you can commit frequently.
Three corporate mini-stories from the write-path trenches
Mini-story 1: The incident caused by a wrong assumption
They ran an ecommerce marketplace with seasonal spikes. The team migrated read traffic to replicas and told the business the primary would “only handle writes.” It sounded clean: primary for writes, replicas for reads, everyone’s happy.
The wrong assumption was subtle: they assumed “reads are safe anywhere.” But their checkout flow read inventory availability and coupon validity from the replica to reduce load. Under normal lag—seconds—it mostly worked. During a flash sale, replication lag climbed. The replica showed inventory still available. Customers kept checking out. The primary was correctly rejecting some updates, but the application had already promised availability and applied discounts based on stale reads.
Support got flooded with “why was I charged if you said it was in stock?” Engineering stared at graphs. The primary wasn’t down. CPU was fine. The replica was “green” except for one metric nobody paged on: lag.
The fix was not heroic tuning. They changed policy: any read that influences a write decision (inventory, coupon redemption, payment state) must be read-your-writes consistent. In practice: read from primary or from a quorum-consistent store, and cache only after commit. They also added a hard cutoff: if replica lag exceeds a threshold, the app stops using replicas for those endpoints.
The postmortem lesson was blunt: stale reads are a correctness bug, not a performance feature. Replicas are for scale, not for pretending time is optional.
Mini-story 2: The optimization that backfired
A retailer on MariaDB had a nasty p99 at checkout. Someone found the usual suspects: too many fsyncs, commit latency, and storage that was fine but not thrilled. They “optimized” by relaxing durability settings. Commits got faster. Everyone congratulated each other and went back to shipping features.
Two months later, a kernel panic took out the primary. Failover happened. The site stayed up. And then the weirdness started: a handful of orders existed in the application logs but not in the database; some payments were captured but the order state machine was missing steps; support had to manually reconcile state using payment provider reports.
Nothing like discovering you built a small accounting system where “eventually consistent” means “eventually someone cries.”
They rolled durability back for the checkout schema and moved performance work to safer layers: fewer indexes on hot tables, smaller transactions, and explicit queueing of inventory reservations. The real win came from reducing lock contention and write amplification—not from pretending disks never fail.
Mini-story 3: The boring but correct practice that saved the day
A subscription commerce company ran Postgres. They weren’t fancy. They were disciplined. Every quarter, they ran a “write-path fire drill”: simulate a spike, verify alerts, and rehearse the playbook. They also had a dull-sounding rule: no DDL during peak, and every schema change must be staged with a reversible plan.
One afternoon, a marketing campaign went better than expected. Writes surged. Latency rose but stayed bounded. Then one replica started lagging badly. The on-call didn’t debate philosophy; they followed the checklist: route critical reads to primary, shed nonessential write features (wishlist events, browsing history), and watch WAL/checkpoints.
They found the real issue: a background job had started updating a large fraction of a hot table, creating vacuum debt. Because they tracked dead tuples and autovacuum behavior, it was obvious. They paused the job, let autovacuum recover, and then reintroduced the job with batching and a lower priority schedule.
The incident never made it to customers. Not because Postgres is magical. Because someone did the boring work: visibility, drills, and controlled changes.
Common mistakes: symptoms → root cause → fix
1) Symptom: “Database is slow” but CPU is low
Root cause: lock queues or IO waits. The database is not computing; it’s waiting.
Fix: In Postgres, inspect pg_stat_activity wait events and blocking PIDs; kill the blocker if necessary. In MariaDB, check InnoDB status for lock waits/deadlocks. Then shorten transactions and reduce hot-row updates.
2) Symptom: p99 spikes every few minutes
Root cause: checkpoint/flush behavior (Postgres checkpoints, MariaDB flush storms), or periodic background jobs.
Fix: Spread IO with checkpoint tuning (Postgres) and ensure WAL/data separation where possible. For MariaDB, verify redo log sizing and durability settings, and audit background jobs for bursty updates.
3) Symptom: replicas are “up” but customers see inconsistent state
Root cause: replication lag and app reads from replicas for correctness-adjacent logic.
Fix: Enforce read-your-writes for checkout and inventory. Add lag-aware routing. Add idempotency so retries don’t duplicate writes.
4) Symptom: deadlocks spike during promotions
Root cause: hot rows + inconsistent lock ordering + aggressive retries.
Fix: Standardize lock order, reduce transaction scope, move contention into a queue/allocator, and implement exponential backoff with jitter on retries.
5) Symptom: Postgres disk usage climbs, queries get slower week over week
Root cause: bloat from updates; autovacuum behind; indexes growing.
Fix: Tune autovacuum per table, reduce update churn, consider partitioning and fillfactor adjustments. If bloat is severe, schedule a controlled rewrite (vacuum full / reindex / table rebuild) with a downtime or online strategy.
6) Symptom: MariaDB commits slow, but reads are fine
Root cause: redo/binlog flush pressure, fsync latency, or binlog group commit not keeping up.
Fix: Validate storage fsync performance. Ensure you’re not sharing redo/binlog with noisy neighbors. Keep transactions small. If replication is enabled, verify binlog settings match durability requirements.
7) Symptom: Postgres autovacuum runs “all the time” and still can’t catch up
Root cause: write amplification from excessive indexes and frequent updates; too-small maintenance work memory; long-running transactions preventing cleanup.
Fix: Remove unused indexes, break up update jobs, fix long transactions, and tune autovacuum workers/costs. Also check for “idle in transaction.”
Joke #2: If your fix is “increase max_connections,” you’re basically solving a traffic jam by adding more cars.
Checklists / step-by-step plan
Step-by-step: making ecommerce writes boring (the goal)
- Define correctness boundaries. Identify reads that influence purchase decisions (inventory, coupon redemption, payment status). Force them to be consistent with writes.
- Shorten transactions. No network calls inside DB transactions. If you must coordinate, use idempotency keys and a state machine with frequent commits.
- Remove index vanity. For write-heavy tables, keep only indexes that pay rent. Validate with query stats, not opinions.
- Partition append-only monsters. Orders/events/audit tables should be partitioned by time or tenant if they grow fast and are queried by recent ranges.
- Make hot rows rare. Replace global counters with bucketed counters. Replace “inventory row update per item” with reservation allocation or event sourcing plus projection.
- Backpressure over backlog. Rate-limit checkout writes at the edge when the DB is stressed. A controlled “try again” beats a cascading failure.
- Instrument the write path. Track commit latency, lock wait time, replication lag, and queue depth at the application layer.
- Design retries as part of the system. Retries must be idempotent and jittered, or you will create your own outage.
- Separate OLTP from analytics. Reporting queries and “export everything” jobs shouldn’t share the same lock and IO budget as checkout.
- Practice failover and lag response. You don’t want to invent your response to lag during a promotion.
What I’d do if choosing today for a write-heavy ecommerce core
- If you have strong ops maturity and want deep introspection and robust transactional semantics: PostgreSQL, with serious attention to WAL/checkpoints and vacuum from day one.
- If your organization is already MySQL/MariaDB-native and you can enforce transaction discipline and replication hygiene: MariaDB, but treat replication lag as a first-class SLO and do not “optimize” away durability for money-moving writes.
FAQ
1) Is PostgreSQL always better for write-heavy workloads?
No. Postgres is often easier to reason about when you instrument it well, but it can absolutely fall over from WAL/IO limits, vacuum debt, or connection overload. Write-heavy success is mostly about transaction shape and IO capacity.
2) Is MariaDB always faster at inserts?
Sometimes, especially with simple schemas and the right durability tradeoffs. But ecommerce writes aren’t just inserts—they’re inserts plus indexes plus constraints plus replication plus hot-row updates. “Faster inserts” is not the same as “stable checkout under contention.”
3) What’s the #1 reason Postgres checkout writes get slow?
In production: IO pressure around WAL/checkpoints combined with bloat or vacuum falling behind. Lock chains are a close second, usually caused by long transactions or misguided locking reads.
4) What’s the #1 reason MariaDB checkout writes get slow?
Commit/flush pressure plus contention. Then replication lag turns your architecture into a correctness bug factory if you read from replicas for decisions.
5) Should I use UUID primary keys for orders?
You can, but understand the write cost. Random UUIDs can increase page splits and reduce locality (especially painful with clustered storage like InnoDB). If you need globally unique IDs, consider time-ordered UUID variants or separate surrogate keys where appropriate.
6) Can I “solve” hot inventory rows with SELECT FOR UPDATE?
You can serialize correctly, but you may also serialize throughput. Locking is correctness, not performance. The scalable fix is reducing contention: allocate inventory in batches, shard per warehouse/bucket, or use a reservation service that controls concurrency.
7) Do I need connection pooling?
For Postgres: almost always, yes, at scale. For MariaDB: it’s still beneficial, but the pain threshold is usually different. Either way, uncontrolled connection growth is how latency becomes a mystery novel.
8) Is asynchronous replication acceptable for ecommerce?
Yes, if you treat it as asynchronous. That means: don’t read from replicas for decisions that affect money or inventory unless you tolerate staleness and have compensating controls. Many teams say they tolerate it; their support queues disagree.
9) What’s the safest performance improvement for write-heavy checkout?
Reduce write amplification: fewer indexes on hot tables, smaller transactions, and moving nonessential writes off the critical path (event queues). Storage upgrades help too, but they don’t fix bad write patterns.
10) Which database gives better tooling for diagnosing write bottlenecks?
Postgres generally has stronger built-in introspection around waits, WAL, and vacuum behavior. MariaDB has good tools too, but you’ll often rely more on InnoDB status snapshots and replication metrics. Either way, the tooling only matters if you actually alert on the right things.
Next steps you can execute
If you’re running write-heavy ecommerce today and you want fewer surprises, do these in order:
- Map your checkout transaction boundaries and remove any external calls inside them. Make the workflow idempotent.
- Identify hot rows (inventory, coupons, counters) and redesign them to avoid single-row contention.
- Audit indexes on the top 5 write-heavy tables. Remove anything not required for critical reads.
- Instrument the bottlenecks: Postgres wait events/WAL/checkpoints/vacuum; MariaDB deadlocks/redo/binlog/replication lag.
- Implement lag-aware routing so replicas can never silently corrupt business logic.
- Run a load test that looks like real life: spiky, skewed to a few hot SKUs, with retries and timeouts. Your database doesn’t care about your average QPS.
After that, the MariaDB vs PostgreSQL choice becomes clearer. Not because one is “better,” but because you’ll finally know what kind of pain you’re buying—and how to keep it from reaching checkout.