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Yes. At this point, the next phase should be **systematic failure testing**. A multi-master cluster is not production-ready just because `INSERT` on Node1 appears on Node2 and Node3.
One important thing first: your setup has been using **Spock 5.0.1**. pgEdge's current v5 release notes list **5.0.12**, with fixes around connection-loss recovery, exception handling, apply workers, memory growth and newer PostgreSQL security changes. Before production, I would include a controlled upgrade rehearsal to a current supported 5.x patch rather than certify 5.0.1 as-is. [pgEdge Documentation](https://docs.pgedge.com/spock-v5/development/spock_release_notes/?utm_source=chatgpt.com)
Do the destructive tests below on a staging copy of the cluster, not on the only copy of important data.
## 1. Establish your normal baseline first
Before deliberately breaking anything, all three nodes should show healthy subscriptions and essentially zero lag.
Run on every node:
```sql
SELECT * FROM spock.sub_show_status();
```
```sql
SELECT * FROM spock.lag_tracker;
```
```sql
SELECT
slot_name,
active,
wal_status,
restart_lsn,
confirmed_flush_lsn
FROM pg_replication_slots;
```
Check:
```sql
SHOW track_commit_timestamp;
SHOW spock.conflict_resolution;
SHOW spock.exception_behaviour;
```
And:
```sql
SELECT *
FROM spock.exception_log
ORDER BY timestamp DESC
LIMIT 50;
```
`track_commit_timestamp` needs to be enabled for timestamp-based Spock conflict resolution such as `last_update_wins`. [pgEdge Documentation](https://docs.pgedge.com/spock-v5/development/configuring/?utm_source=chatgpt.com)
Your baseline should be:
```text
Node1 → Node2 ✓
Node1 → Node3 ✓
Node2 → Node1 ✓
Node2 → Node3 ✓
Node3 → Node1 ✓
Node3 → Node2 ✓
Lag → approximately 0
Exceptions → none unexplained
```
---
# Production test matrix
| ID | Scenario | Test | Production PASS condition |
| --- |--------------------------|-------------------------------------------------------|---------------------------|
| P01 | Node1 INSERT | Insert unique rows on Node1 | Node2 + Node3 receive exact rows |(pass)
| P02 | Node2 INSERT | Write only Node2 | Node1 + Node3 receive them |(pass)
| P03 | Node3 INSERT | Write only Node3 | Node1 + Node2 receive them |(pass)
| P04 | UPDATE/DELETE | Perform updates/deletes from every node | All 3 converge |(pass)
| P05 | PostgreSQL restart | Restart one PostgreSQL container | Replication reconnects automatically | (pass)
| P06 | VM reboot | Reboot one VM completely | DB + Spock recover and catch up |(pass)
| P07 | Short outage | Node offline 5–30 min while others write | All backlog catches up |(inprogress)
| P08 | Long backlog | Generate hours/days-equivalent WAL while node offline | Catch-up succeeds without WAL loss/disk exhaustion |
| P09 | Network partition | Node stays writable but cannot replicate | Reconnect works and conflicts behave as designed |
| P10 | Same-row UPDATE | Modify same PK concurrently on 2 nodes | Expected conflict policy wins |
| P11 | Duplicate PK | Insert same PK independently on 2 nodes | Conflict detected/resolved as expected |
| P12 | Unique constraint | Different PKs but same UNIQUE value | Failure/conflict is handled and alerted |
| P13 | DELETE vs UPDATE | Delete a row on one node, update on another | Expected result + exception handling verified |
| P14 | Business conflict | Two nodes decrement same inventory/balance | Business invariant remains correct or architecture changed |
| P15 | DDL | CREATE/ALTER during maintenance procedure | Identical schema across all nodes |
| P16 | Node-down DDL | One node unavailable during schema migration | Recovery procedure proven |
| P17 | Schema mismatch | Deliberately introduce mismatch in staging | Monitoring detects it; recovery works |
| P18 | Disk pressure | Drive disk toward threshold in staging | Alerts fire before PostgreSQL becomes unsafe |
| P19 | Replication-slot backlog | Subscriber offline while WAL grows | Retention monitored and bounded |
| P20 | Lost slot/WAL | Simulate unrecoverable replication state | Documented resync procedure succeeds |
| P21 | High write load | Concurrent writes across all nodes | Lag remains inside target |
| P22 | Long soak | 24–72 h realistic traffic | No growing lag/memory/disk/errors |
| P23 | ACE comparison | Run table-diff after every failure test | Tables match |
| P24 | Backup restore | Restore pgBackRest backup | Database boots and data verified |
| P25 | PITR | Recover to timestamp before accidental change | Recovery reaches correct point |
| P26 | Entire-site loss | Lose one VM/site completely | Remaining service meets RTO/RPO |
| P27 | Credentials/TLS | Invalid clients attempt connection | Unauthorized traffic rejected |
| P28 | Upgrade | PostgreSQL/Spock patch upgrade rehearsal | Cluster remains consistent |
| P29 | Monitoring failure | Kill exporter/agent | Alert tells you monitoring itself failed |
| P30 | Control-plane failure | Stop your FastAPI management service | Database replication continues independently |
That is the set I would use as your production qualification.
---
## 2. The first conflict test: same row, two nodes
Create:
```sql
CREATE TABLE conflict_test (
id UUID PRIMARY KEY,
value TEXT,
updated_at TIMESTAMPTZ DEFAULT now()
);
```
Insert one row and allow it to synchronize.
Then disconnect replication temporarily in your staging test and make:
Node1:
```sql
UPDATE conflict_test
SET value = 'NODE1'
WHERE id = '<same-uuid>';
```
Node2:
```sql
UPDATE conflict_test
SET value = 'NODE2'
WHERE id = '<same-uuid>';
```
Restore connectivity.
Eventually:
```sql
SELECT * FROM conflict_test;
```
should give the **same value on all three nodes**.
But the exact winner needs to match your chosen conflict policy.
Spock 5.x supports policies including:
```text
error
apply_remote
keep_local
last_update_wins
```
with `last_update_wins` using commit timestamps. [pgEdge Documentation](https://docs.pgedge.com/spock-v5/development/configuring/?utm_source=chatgpt.com)
This test answers:
> "Does replication converge when two masters genuinely disagree?"
---
## 3. Duplicate primary-key test
This is extremely important.
While two nodes are isolated:
Node1:
```sql
INSERT INTO conflict_test(id, value)
VALUES ('same-uuid', 'NODE1');
```
Node2:
```sql
INSERT INTO conflict_test(id, value)
VALUES ('same-uuid', 'NODE2');
```
Reconnect.
All nodes should eventually converge according to the configured conflict-resolution policy.
If you're using generated integer IDs, test those particularly aggressively.
For active-active, UUID/Snowflake-style distributed IDs are much safer than independent ordinary sequences.
---
## 4. Test a second UNIQUE constraint
This is one of the places active-active systems get interesting.
Example:
```sql
CREATE TABLE user_test (
id UUID PRIMARY KEY,
email TEXT UNIQUE
);
```
Node1:
```sql
INSERT INTO user_test
VALUES (
gen_random_uuid(),
'test@example.com'
);
```
Node2 simultaneously:
```sql
INSERT INTO user_test
VALUES (
gen_random_uuid(),
'test@example.com'
);
```
Different primary keys:
```text
UUID A ≠ UUID B
```
but identical:
```text
email
```
Spock 5 documentation specifically warns about multiple unique indexes/constraints in multi-master environments because conflict handling can encounter downstream constraint violations. [pgEdge Documentation](https://docs.pgedge.com/spock-v5/v5-0-10/limitations/?utm_source=chatgpt.com)
You need to know exactly how your application's tables behave here.
---
# 5. Network partition — one of your most important tests
This is different from shutting Node1 down.
### Clean outage
```text
Node1
OFF
Node2 ←→ Node3
```
Generally easy:
```text
Node2/3 accumulate changes
↓
Node1 returns
↓
backlog catches up
```
### Network partition
Much more dangerous:
```text
Node1 Node2/Node3
ONLINE ONLINE
writes writes
X network connection X
```
Now both sides are independently accepting transactions.
Suppose:
```text
Node1:
customer balance = 50
Node2:
customer balance = 75
```
After reconnection, Spock has to reconcile those histories.
Your PASS condition isn't merely:
```text
replication resumed
```
It is:
```text
✓ replication resumed
✓ conflict policy executed correctly
✓ no subscription disabled
✓ exception log understood
✓ lag returned to zero
✓ ACE says tables agree
✓ application business rules still hold
```
---
# 6. Test business conflicts, not just database conflicts
This is possibly **the most important production test**.
Start with:
```text
inventory = 1
```
During a partition:
Node1:
```sql
UPDATE products
SET inventory = inventory - 1
WHERE id = 10;
```
Node2:
```sql
UPDATE products
SET inventory = inventory - 1
WHERE id = 10;
```
Both transactions locally see:
```text
1 → 0
```
Eventually the database might converge to:
```text
inventory = 0
```
Yet:
```text
two customers bought one item
```
That's a failure at the **business layer**, even though replication technically worked.
Spock also provides delta-apply mechanisms specifically because counters/running totals are a special problem in active-active designs. [pgEdge Documentation](https://docs.pgedge.com/spock-v6/v6-0-0-beta/conflicts/?utm_source=chatgpt.com)
For things such as:
```text
bank balances
inventory
seat availability
quotas
ticket allocation
counters
```
you must test the actual business invariant.
---
# 7. Ten-day outage / WAL-retention test
You don't necessarily have to wait ten real days.
Generate a representative amount of WAL while one node is offline.
Monitor:
```sql
SELECT
slot_name,
active,
wal_status,
pg_size_pretty(
pg_wal_lsn_diff(
pg_current_wal_lsn(),
restart_lsn
)
) AS retained_wal
FROM pg_replication_slots;
```
You want to know:
```text
100 MB backlog?
1 GB?
20 GB?
100 GB?
```
Then calculate how much storage you'd need for your actual outage window.
Pass condition:
```text
Node offline
↓
WAL grows
↓
disk remains safe
↓
node returns
↓
lag decreases steadily
↓
0 lag
↓
ACE passes
```
---
# 8. Disk-full test
You already discovered how unpleasant a `100% /` filesystem can be.
For production, don't wait until:
```text
100%
```
Alert at something like:
```text
70% warning
80% serious
90% critical
```
based on your actual capacity model.
Especially monitor:
```text
/
PGDATA
pg_wal
Docker storage
backup repository
```
A lagging replication slot can cause WAL retention to grow dramatically.
Do the actual "nearly fill the disk" experiment only on staging.
---
# 9. DDL testing
This deserves an entire test category.
Test:
```sql
CREATE TABLE
ALTER TABLE ADD COLUMN
ALTER TABLE DROP COLUMN
CREATE INDEX
DROP INDEX
ALTER COLUMN TYPE
DROP TABLE
```
But be careful.
Spock's own v5 documentation says multi-master schema changes require special care because there's no generic "freeze replication queue and wait until every transaction is applied" mechanism. For multi-master, writes should be stopped on all nodes and slots allowed to catch up before incompatible schema changes. [pgEdge Documentation](https://docs.pgedge.com/spock-v5/v5-0-10/limitations/?utm_source=chatgpt.com)
It also specifically warns about operations including:
```text
CREATE TABLE AS
DROP TABLE in 3+ node clusters
```
and recommends maintenance windows for potentially dangerous DDL. [pgEdge Documentation](https://docs.pgedge.com/spock-v5/development/managing/spock_autoddl/?utm_source=chatgpt.com)
So test your real production migration procedure:
```text
Application maintenance/read-only
↓
stop writes
↓
lag → zero
↓
perform DDL
↓
verify schema all nodes
↓
ACE
↓
resume traffic
```
That procedure itself should be documented.
---
# 10. Kill PostgreSQL brutally
Not just:
```bash
docker stop
```
which is relatively graceful.
On a disposable/staging node, simulate:
```text
process crash
container crash
VM crash
unexpected host reboot
```
Then verify:
```text
PostgreSQL crash recovery
↓
Spock starts
↓
subscriptions reconnect
↓
backlog catches up
↓
ACE matches
```
You want proof that an administrator doesn't need to manually rebuild replication after an ordinary crash.
---
# 11. Restart nodes repeatedly
Try:
```text
Restart Node1
Restart Node2
Restart Node3
Restart Node1 + Node2
Restart Node2 + Node3
Restart all three
```
Then vary the order in which they come back.
Example:
```text
Node3
↓
Node1
↓
Node2
```
The cluster should recover regardless of reasonable startup ordering.
---
# 12. Test an actual replication exception
You should deliberately create something that lands in:
```sql
spock.exception_log
```
Then prove your operational process.
Monitor:
```sql
SELECT *
FROM spock.exception_log
ORDER BY timestamp DESC
LIMIT 50;
```
Spock's exception behavior can be configured to discard an offending statement/transaction or disable a subscription, so your team needs to know exactly which policy you're using and what happens afterward. [pgEdge Documentation](https://docs.pgedge.com/spock-v5/development/troubleshooting/?utm_source=chatgpt.com)
The test isn't complete until you demonstrate:
```text
alert
↓
diagnosis
↓
repair
↓
subscription healthy
↓
ACE validation
```
---
# 13. ACE after EVERY major failure test
This is important.
Replication saying:
```text
replicating
```
doesn't prove:
```text
data identical
```
Run:
```bash
./ace table-diff cluster1 public.your_table
```
ACE compares table contents across nodes and reports actual differences. [pgEdge Documentation](https://docs.pgedge.com/ace/v2-1-1/commands/diff/table-diff/?utm_source=chatgpt.com)
For each critical table, after:
```text
partition
restart
conflict
DDL
recovery
upgrade
```
you should end with:
```text
TABLES MATCH
```
---
# 14. Heavy-load test
Your system may work perfectly at:
```text
10 transactions/sec
```
and fall apart at:
```text
2,000 transactions/sec
```
Generate simultaneous:
```text
INSERT
UPDATE
DELETE
SELECT
```
across all three nodes.
Monitor:
```sql
SELECT * FROM spock.lag_tracker;
```
`spock.lag_tracker` provides replication lag in both bytes and time. [pgEdge Documentation](https://docs.pgedge.com/spock-v5/v5-0-9/monitoring/lag_tracking/?utm_source=chatgpt.com)
Monitor additionally:
```text
CPU
memory
disk IOPS
network bandwidth
connections
TPS
locks
deadlocks
WAL/sec
replication lag
slot backlog
```
You already have much of this in your monitoring/FastAPI work.
---
# 15. Resource-exhaustion test
Check:
```sql
SHOW max_worker_processes;
SHOW max_replication_slots;
SHOW max_wal_senders;
SHOW max_connections;
```
Don't assume:
```conf
max_replication_slots = 10
max_wal_senders = 10
```
is automatically a production number just because the lab works.
Spock uses workers, replication slots, walsenders and replication origins, and insufficient capacity can prevent replication workers from starting. Current pgEdge sizing guidance explicitly warns that tutorial/default values should not be treated as universal production sizing. [pgEdge Documentation](https://docs.pgedge.com/spock-v6/development/sizing/?utm_source=chatgpt.com)
Run load tests until you know your headroom.
---
# 16. Backup is not tested until restore works
Install/configure pgBackRest and test:
```text
Full backup
↓
Incremental backup
↓
WAL archive
↓
restore
```
Then verify the restored database.
pgEdge recommends monitoring pgBackRest backups and explicitly emphasizes that disaster-recovery strategy needs to be tested and customized for the database. [pgEdge Documentation](https://docs.pgedge.com/platform/managing/pgbackrest/?utm_source=chatgpt.com)
---
# 17. PITR test
Simulate:
```text
10:00 good data
10:15 good data
10:20 accidental DELETE
10:25 users notice
```
You need to demonstrate:
```text
restore → 10:19
```
pgBackRest supports recovery to a specific timestamp/LSN/XID/recovery point. [pgEdge Documentation](https://docs.pgedge.com/pgbackrest/development/user-guide/pitr/?utm_source=chatgpt.com)
Measure:
```text
RPO = how much data you lost
RTO = how long until service returned
```
Don't estimate those numbers.
Actually measure them.
---
# 18. Upgrade test
Given your older Spock patch level, this is especially important.
Clone production-like data and perform:
```text
PostgreSQL minor update
Spock patch update
Docker image update
OS security update
```
Then:
```text
restart
↓
subscriptions healthy
↓
load test
↓
conflict test
↓
ACE
```
Current Spock 5.x patch releases contain fixes specifically involving reconnect/recovery and replication behavior, which are exactly the areas you're testing. [pgEdge Documentation](https://docs.pgedge.com/spock-v5/development/spock_release_notes/?utm_source=chatgpt.com)
---
# 19. Security tests
For production, verify:
```text
✓ PostgreSQL replication traffic encrypted
✓ application DB connections encrypted
✓ SCRAM/cert authentication
✓ pg_hba restricts source networks
✓ replication account permissions controlled
✓ database superuser credentials aren't in frontend
✓ FastAPI secrets aren't hard-coded
✓ JWT expires/validates correctly
✓ Docker API isn't publicly exposed
✓ FastAPI Docker-control endpoints require authorization
✓ logs don't leak passwords
```
Your Docker-management API is particularly sensitive because Docker daemon access is effectively host-administrative access.
---
# 20. Monitoring failure itself
People often forget this one.
Kill:
```text
postgres_exporter
Prometheus
FastAPI monitoring agent
```
Your system should tell you:
> Monitoring is unavailable.
Otherwise this can happen:
```text
Monitoring dies
↓
everything looks green
↓
database fails
↓
nobody knew
```
---
## Build these six dashboards
For production I would make your FastAPI/React monitoring page show:
```text
CLUSTER HEALTH
Node1 ONLINE
Node2 ONLINE
Node3 ONLINE
REPLICATION
Node1 → Node2 0 MB / 20 ms
Node1 → Node3 0 MB / 25 ms
Node2 → Node1 0 MB / 18 ms
Node2 → Node3 0 MB / 21 ms
Node3 → Node1 0 MB / 23 ms
Node3 → Node2 0 MB / 19 ms
SLOTS
slot active retained WAL
node1_from_node2 yes 0 MB
node1_from_node3 yes 0 MB
CONFLICTS
last hour: 0
EXCEPTIONS
unresolved: 0
SYSTEM
CPU 28%
RAM 61%
Disk 52%
Connections 42 / 200
WAL 3.1 GB
ACE
Last verification:
✓ TABLES MATCH
```
That would turn your FastAPI project into a genuinely useful management plane rather than just an API collection.
---
# Your final go-live gate
I would **not call the system production-ready** until you can demonstrate all of these:
1. Every node can write and replicate to every other node.
2. A node can disappear and automatically catch up.
3. A writable network partition has been tested.
4. Concurrent-write conflicts behave predictably.
5. Application business invariants survive concurrent writes.
6. WAL backlog cannot silently fill the disk.
7. Replication errors generate alerts.
8. DDL has a documented maintenance procedure.
9. ACE confirms convergence after failures.
10. Backup restore actually works.
11. PITR actually works.
12. RPO and RTO have been measured.
13. High-load and long-duration tests pass.
14. Security/TLS/authentication have been verified.
15. Upgrade and rollback procedures have been rehearsed.
16. Every failure has a written recovery runbook.
If those pass repeatedly, then you're getting into **actual production-grade active-active territory**, rather than simply having a working three-node Spock cluster.