Failure Model

• 1: Model of Patroni Passive Failure
• 2: Model of Patroni Active Failure

flowchart LR
    A([Primary Failure]) --> B{Patroni<br/>Detected?}

    B -->|PG Crash| C[Attempt Local Restart]
    B -->|Node Down| D[Wait TTL Expiration]

    C -->|Success| E([Local Recovery])
    C -->|Fail/Timeout| F[Release Leader Lock]

    D --> F
    F --> G[Replica Election]
    G --> H[Execute Promote]
    H --> I[HAProxy Detects]
    I --> J([Service Restored])

    style A fill:#dc3545,stroke:#b02a37,color:#fff
    style E fill:#198754,stroke:#146c43,color:#fff
    style J fill:#198754,stroke:#146c43,color:#fff

1 - Model of Patroni Passive Failure

RTO Timeline

Failure Model

Key Difference Between Passive and Active Failover:

In passive failover scenarios, Patroni dies along with the node and cannot actively release the Leader Key. The lease in DCS can only trigger cluster election after TTL naturally expires.

Timeline Analysis

Phase 1: Lease Expiration

The Patroni primary refreshes the Leader Key every loop_wait cycle, resetting TTL to the configured value.

Timeline:
     t-loop        t          t+ttl-loop    t+ttl
       |           |              |           |
    Last Refresh  Failure      Best Case   Worst Case
       |←── loop ──→|              |           |
       |←──────────── ttl ─────────────────────→|

• Best case: Failure occurs just before lease refresh (elapsed loop since last refresh), remaining TTL = ttl - loop
• Worst case: Failure occurs right after lease refresh, must wait full ttl
• Average case: ttl - loop/2

Phase 2: Replica Detection

Replicas wake up on loop_wait cycles and check the Leader Key status in DCS.

Timeline:
    Lease Expired   Replica Wakes
       |            |
       |←── 0~loop ─→|

• Best case: Replica happens to wake when lease expires, wait 0
• Worst case: Replica just entered sleep when lease expires, wait loop
• Average case: loop/2

Phase 3: Lock Contest & Promote

When replicas detect Leader Key expiration, they start the election process. The replica that acquires the Leader Key executes pg_ctl promote to become the new primary.

1. Via REST API, parallel queries to check each replica’s replication position, typically 10ms, hardcoded 2s timeout.
2. Compare WAL positions to determine the best candidate, replicas attempt to create Leader Key (CAS atomic operation)
3. Execute pg_ctl promote to become primary (very fast, typically negligible)

Election Flow:
  ReplicaA ──→ Query replication position ──→ Compare ──→ Contest lock ──→ Success
  ReplicaB ──→ Query replication position ──→ Compare ──→ Contest lock ──→ Fail

• Best case: Single replica or immediate lock acquisition and promotion, constant overhead 0.1s
• Worst case: DCS API call timeout: 2s
• Average case: 1s constant overhead

Phase 4: Health Check

HAProxy detects the new primary online, requiring rise consecutive successful health checks.

Detection Timeline:
  New Primary    First Check   Second Check  Third Check (UP)
     |          |           |           |
     |←─ 0~inter ─→|←─ fast ─→|←─ fast ─→|

• Best case: New primary promoted just before check, (rise-1) × fastinter
• Worst case: New primary promoted right after check, (rise-1) × fastinter + inter
• Average case: (rise-1) × fastinter + inter/2

RTO Formula

Sum all phase times to get total RTO:

Best Case

Average Case

Worst Case

Model Calculation

Substitute the four RTO model parameters into the formulas above:

pg_rto_plan:  # [ttl, loop, retry, start, margin, inter, fastinter, downinter, rise, fall]
  fast: [ 20  ,5  ,5  ,15 ,5  ,'1s' ,'0.5s' ,'1s' ,3 ,3 ]  # rto < 30s
  norm: [ 30  ,5  ,10 ,25 ,5  ,'2s' ,'1s'   ,'2s' ,3 ,3 ]  # rto < 45s
  safe: [ 60  ,10 ,20 ,45 ,10 ,'3s' ,'1.5s' ,'3s' ,3 ,3 ]  # rto < 90s
  wide: [ 120 ,20 ,30 ,95 ,15 ,'4s' ,'2s'   ,'4s' ,3 ,3 ]  # rto < 150s

Four Mode Calculation Results (unit: seconds, format: min / avg / max)

2 - Model of Patroni Active Failure

RTO Timeline

Failure Model

Key Difference Between Active and Passive Failure:

In active failure scenarios, Patroni remains alive and can actively detect PG crash and attempt restart. If restart succeeds, service self-heals; if timeout expires without recovery, Patroni actively releases the Leader Key, triggering cluster election.

Timing Analysis

Phase 1: Failure Detection

Patroni checks PostgreSQL status every loop_wait cycle (via pg_isready or process check).

Timeline:
    Last check      PG crash      Next check
       |              |              |
       |←── 0~loop ──→|              |

• Best case: PG crashes right before Patroni check, detected immediately, wait 0
• Worst case: PG crashes right after check, wait for next cycle, wait loop
• Average case: loop/2

Phase 2: Restart Timeout

After Patroni detects PG crash, it attempts to restart PostgreSQL. This phase has two possible outcomes:

Timeline:
  Crash detected     Restart attempt     Success/Timeout
      |                  |                    |
      |←──── 0 ~ start ─────────────────────→|

Path A: Self-healing Success (Best case)

• PG restarts successfully, service recovers
• No failover triggered, extremely short RTO
• Wait time: 0 (relative to Failover path)

Path B: Failover Required (Average/Worst case)

• PG still not recovered after primary_start_timeout
• Patroni actively releases Leader Key
• Wait time: start

Note: Average case assumes failover is required. If PG can quickly self-heal, overall RTO will be significantly lower.

Phase 3: Standby Detection

Standbys wake up on loop_wait cycle and check Leader Key status in DCS. When primary Patroni releases the Leader Key, standbys discover this and begin election.

Timeline:
    Lease released    Standby wakes
       |                  |
       |←── 0~loop ──────→|

• Best case: Standby wakes right when lease is released, wait 0
• Worst case: Standby just went to sleep when lease released, wait loop
• Average case: loop/2

Phase 4: Lock & Promote

After standbys discover Leader Key vacancy, election begins. The standby that acquires the Leader Key executes pg_ctl promote to become the new primary.

1. Via REST API, parallel queries to check each standby’s replication position, typically 10ms, hardcoded 2s timeout.
2. Compare WAL positions to determine best candidate, standbys attempt to create Leader Key (CAS atomic operation)
3. Execute pg_ctl promote to become primary (very fast, typically negligible)

Election process:
  StandbyA ──→ Query replication position ──→ Compare ──→ Try lock ──→ Success
  StandbyB ──→ Query replication position ──→ Compare ──→ Try lock ──→ Fail

• Best case: Single standby or direct lock acquisition and promote, constant overhead 0.1s
• Worst case: DCS API call timeout: 2s
• Average case: 1s constant overhead

Phase 5: Health Check

HAProxy detects new primary online, requires rise consecutive successful health checks.

Check timeline:
  New primary    First check    Second check   Third check (UP)
     |              |               |               |
     |←─ 0~inter ──→|←─── fast ────→|←─── fast ────→|

• Best case: New primary comes up right at check time, (rise-1) × fastinter
• Worst case: New primary comes up right after check, (rise-1) × fastinter + inter
• Average case: (rise-1) × fastinter + inter/2

RTO Formula

Sum all phase times to get total RTO:

Best Case (PG instant self-healing)

Average Case (Failover required)

Worst Case

Model Calculation

Substituting the four RTO model parameters into the formulas above:

pg_rto_plan:  # [ttl, loop, retry, start, margin, inter, fastinter, downinter, rise, fall]
  fast: [ 20  ,5  ,5  ,15 ,5  ,'1s' ,'0.5s' ,'1s' ,3 ,3 ]  # rto < 30s
  norm: [ 30  ,5  ,10 ,25 ,5  ,'2s' ,'1s'   ,'2s' ,3 ,3 ]  # rto < 45s
  safe: [ 60  ,10 ,20 ,45 ,10 ,'3s' ,'1.5s' ,'3s' ,3 ,3 ]  # rto < 90s
  wide: [ 120 ,20 ,30 ,95 ,15 ,'4s' ,'2s'   ,'4s' ,3 ,3 ]  # rto < 150s

Calculation Results for Four Modes (unit: seconds, format: min / avg / max)

Comparison with Passive Failure

RTO Comparison (Average case):

Analysis: In fast and norm modes, active failure RTO is slightly higher than passive failure because it waits for primary_start_timeout (start); but in safe and wide modes, since start < ttl - loop, active failure is actually faster. However, active failure has the possibility of self-healing, with potentially extremely short RTO in best case scenarios.