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Checkout slowed because the host’s container network saturated before either PHP workers or PostgreSQL did. In this marketplace, peak checkout p95 climbed from about 280 ms to more than 2.1 seconds while database queries stayed at 8–14 ms. The combined causes were default bridge-network overhead, a full Linux conntrack table, queued Docker DNS lookups, and a large number of short-lived database and cache connections. Raising conntrack capacity, moving east-west traffic to an intentional user-defined network, caching DNS, and adding PgBouncer reduced peak checkout p95 to 310 ms without a material infrastructure-cost increase. The changes fixed a latency and packet-loss bottleneck, but they did not by themselves remove the single-host failure domain.
What happened to checkout during traffic spikes?
Binadit’s PHP multi-vendor marketplace had run in Docker Compose on one host for roughly a year. It served about 40,000 daily active users and reached nearly 900 requests per second. At ordinary load, checkout p95 was approximately 280 ms. During traffic spikes, it exceeded 2.1 seconds even though PostgreSQL query times remained around 8–14 ms.
Adding more application containers made the delay slightly worse. That result matters: extra replicas increased the number of network flows and lookups without removing the underlying bottleneck. The slow segment was the path between containers, not PHP CPU saturation or SQL execution.
Each checkout performed about three to four internal calls, including Redis, PostgreSQL and search. A small delay on every hop, multiplied by connection churn and DNS work, became a user-visible pause at peak concurrency.
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Why did normal CPU and database graphs miss the problem?
Application and database dashboards measure only part of a request. A checkout can wait in the kernel’s flow-tracking table, in Docker’s name resolver, or on the network driver before a query reaches PostgreSQL. Three effects compounded in this case.
Default bridge networking added latency on every east-west hop
The team measured about 1.8 ms of added latency per inter-container hop on the default bridge path. A checkout touching Redis, PostgreSQL and search can cross that path several times, so the overhead accumulates even when each individual service is healthy. NAT and firewall processing on the host also make the bridge path more work-intensive than a deliberately designed internal network.
Conntrack exhaustion dropped or delayed new flows
The host’s nf_conntrack_max remained at the kernel default of 65,536. Short-lived Redis and PostgreSQL connections filled the table during spikes. Once it was full, the kernel logged nf_conntrack: table full, dropping packet; new connections could be delayed or fail outright. This explains why a request can report a generic connection error while CPU, memory and SQL execution charts look normal.
Google Cloud’s GKE guidance describes the same general failure mode: conntrack table exhaustion can manifest as a failure. Increasing the table limit or using a local DNS cache where appropriate are separate mitigations; neither substitutes for measuring the table on the affected host.
Docker’s embedded DNS queued lookups
Containers resolve service names through Docker’s embedded resolver at 127.0.0.11. Under heavy connection churn, repeated lookups made resolver latency visible. The application had no effective name-cache layer, so creating many short-lived connections also created repeated DNS work. Internal DNS p95 reached 41 ms at peak, enough to become a substantial fraction of a sub-second checkout.
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How do you confirm that Docker networking, not PostgreSQL, is the bottleneck?
Measure the host and the request path during a representative spike. A single snapshot taken after traffic subsides can hide a table that was full minutes earlier.
- Compare conntrack usage with its ceiling. Read
/proc/sys/net/netfilter/nf_conntrack_countand/proc/sys/net/netfilter/nf_conntrack_maxrepeatedly while load is high. Alert on the ratio, not only on an absolute count; a smaller host can become unsafe well below 65,536 entries. - Search kernel logs for drops. Check the kernel journal for
nf_conntrack: table full, dropping packet. Correlate the timestamp with failed checkout attempts and the conntrack-usage graph. - Measure service-name resolution separately. Record DNS latency from an application container to
127.0.0.11and track cache hits and misses. Application p95 alone cannot tell a slow lookup from a slow Redis or PostgreSQL operation. - Map the checkout call graph. Count internal calls and network hops to Redis, PostgreSQL and search. Also count connection creations per request; a high number of short-lived flows consumes conntrack entries faster than a stable pool.
- Compare connection and query timings. If PostgreSQL execution remains near 8–14 ms while connect, DNS or socket-wait time expands, investigate the network and pool before changing SQL.
- Inspect network membership and forwarding. Use Docker network inspection to verify which interfaces each service uses, whether public and east-west traffic are separated, and whether host firewall or forwarding rules are interfering.
Run these checks during normal traffic and at least one realistic spike. The useful baseline is peak p95, DNS p95, conntrack utilization and failed internal connections measured together.
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The team changed four host-level settings and added utilization monitoring. The values below describe this case, not universal limits; size them against the host’s memory, expected concurrent flows and traffic pattern.
| Setting | Case-study value | Purpose | Operational note |
|---|---|---|---|
net.netfilter.nf_conntrack_max |
262144 | Raises the number of tracked flows the host can retain. | Monitor actual usage; a larger table delays exhaustion but does not cure connection churn. |
net.netfilter.nf_conntrack_tcp_timeout_established |
600 seconds | Limits how long established TCP entries remain tracked when idle. | Validate that the timeout fits application keep-alives and long-running sessions. |
net.ipv4.tcp_tw_reuse |
1 | Allows eligible TIME_WAIT sockets to be reused for outgoing connections. | Apply only with a kernel and traffic pattern that support the setting safely. |
net.core.somaxconn |
4096 | Raises the listen-backlog ceiling for accepting bursts of connections. | The application and proxy must also be configured to use an adequate backlog. |
Persist settings through the host’s normal sysctl mechanism, roll them out to one host first, and verify the effective values after reboot. Increasing conntrack capacity without monitoring can simply turn an invisible failure into a larger, later failure.
How should Docker networks be redesigned for internal traffic?
The marketplace moved service-to-service traffic to a user-defined bridge network with explicit inter-container communication. Public load-balancer traffic remained on a separate network. This makes the east-west path intentional and keeps public exposure decisions independent from internal service discovery.
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The supplied configuration used an MTU of 9000. Jumbo frames can reduce per-packet overhead only when every interface and link on the path supports that MTU. Check the host, virtual interfaces, load balancer and upstream network before adopting the value; otherwise use a path-wide MTU that avoids fragmentation.
Docker creates firewall rules for bridge networks and uses masquerading. Do not disable those rules as a shortcut. Review forwarding policy, firewall chains and return paths so the host allows the intended internal traffic while preserving isolation. Test service reachability, DNS resolution and packet size after each network change.
How does a DNS cache help without hiding container changes?
A dnsmasq sidecar was placed in front of Docker’s embedded resolver. It used a 1,000-entry cache, a 10-second local TTL and a 5-second negative TTL. That absorbed bursts of repeated lookups while allowing a replaced container to be recognized within one TTL window.
Keep the cache scope limited to internal names and monitor hit rate, lookup p95 and stale-answer incidents. A long TTL can preserve an obsolete container address; a very short TTL recreates the lookup storm. The 10-second and 5-second values are the case’s operating points, not mandatory defaults for every deployment.
Why did PgBouncer improve both database and network behavior?
PgBouncer was added in transaction-pooling mode with max_client_conn = 2000 and default_pool_size = 50. Application clients could open many logical sessions while PgBouncer reused a smaller number of PostgreSQL connections. That reduced connection establishment, DNS lookups and conntrack entries at the same time.
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Transaction pooling has compatibility implications. Session state that must persist across transactions—such as session-level settings, temporary tables or session-bound prepared statements—needs validation or a different pooling mode. Test checkout flows, migrations, background jobs and administrative connections separately before making transaction pooling the default for every client.
What results did the changes produce?
The following figures were reported by the Binadit Tech Team in 2026. They are case-study measurements, not an independently audited benchmark.
| Metric | Before | After |
|---|---|---|
| Checkout p95 at peak | 2,140 ms | 310 ms |
| Checkout p95 at normal load | 280 ms | 195 ms |
| Peak conntrack utilization | 98–100% | 34% |
| Internal DNS p95 | 41 ms | 2 ms |
| Failed or dropped internal connections per peak hour | About 1,900 | About 12 |
| Monthly support tickets mentioning slow checkout | About 65 | About 4 |
Infrastructure cost was essentially unchanged, apart from about €40 per month in additional observability overhead. The strongest evidence is the simultaneous movement in independent signals: checkout latency fell, DNS p95 fell, conntrack utilization fell and failed internal connections nearly disappeared.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Should you move this workload to Kubernetes?
Not as the first response to this symptom. Kubernetes does not remove conntrack, DNS or network-driver limits; it introduces analogous choices involving the CNI, kube-proxy and CoreDNS. Moving the same connection-heavy workload without fixing its flow and lookup behavior can reproduce the incident on a larger platform.
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Choose a broader topology change when the goal is multi-host availability, independent failure domains or operational automation—not merely lower checkout p95. Treat latency remediation and high availability as separate workstreams, with separate acceptance criteria.
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What limitations remain after the immediate fix?
The changes controlled the network bottleneck but initially left every service on one host. A host, kernel or storage failure could still take down checkout. The team later spread the application tier across two hosts and concluded that high availability should have been scoped earlier.
- Keep conntrack, DNS, connection-pool and checkout p95 metrics in the same dashboard.
- Load-test with realistic short-lived connection behavior, not only steady persistent connections.
- Document which services may use transaction pooling and which require session affinity.
- Test container replacement so DNS TTL behavior is visible before a sale event.
- Plan multi-host redundancy separately once the immediate latency cause is controlled.
How should you roll out the fix safely?
- Capture a baseline during normal load and a representative spike: checkout p95, DNS p95, conntrack count and maximum, and failed internal connections.
- Enable monitoring and kernel-log collection before changing limits, so the next event is observable.
- Apply the conntrack and socket settings to one host or maintenance slice; verify effective values and error rates.
- Create the user-defined internal network and attach one service pair at a time. Keep the public-facing network separate.
- Introduce the DNS cache with short, explicit TTLs and compare lookup latency and stale-name incidents.
- Put a representative PostgreSQL client through PgBouncer transaction pooling. Check session-state compatibility before widening the rollout.
- Repeat the same load profile and compare all four baseline metrics, not just application p95.
- Only after latency is stable, design and test a multi-host deployment for availability.
Frequently Asked Questions
Can Docker networking run out of connections even when the application has spare CPU?
Yes. The host can exhaust tracked flows in conntrack or hit socket and listen-backlog limits while CPU remains underused. The decisive checks are flow-table utilization, kernel drop messages and connection-establishment timing during the spike.
How can I tell whether Docker DNS is slowing requests?
Measure service-name lookup latency from inside the affected containers and compare it with cache-hit and cache-miss counts. If lookup p95 rises while Redis, PostgreSQL and search operation times remain stable, DNS is part of the request delay.
Does connection pooling eliminate the need to raise nf_conntrack_max?
No. Pooling reduces connection churn and therefore lowers pressure, but bursts, other services and long-lived flows still consume conntrack entries. Size and monitor the table for the whole host rather than for PostgreSQL alone.
When is a multi-host design worth doing after fixing latency?
Do it when a single host is an unacceptable failure domain or when maintenance must not interrupt checkout. Treat that availability objective separately from the network-tuning work and validate failover behavior under load.
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