Time-Series Database Selection and Operations#

Time-series databases optimize for a specific access pattern: high-volume writes of timestamped data points, queries that aggregate over time ranges, and automatic expiration of old data. Choosing the right one depends on your data model, query patterns, retention requirements, and operational constraints.

When You Need a Time-Series Database#

A dedicated time-series database is justified when you have high write throughput (thousands to millions of data points per second), queries that are predominantly time-range aggregations, and data that has a defined retention period. Common use cases: infrastructure metrics, application performance monitoring, IoT sensor data, financial tick data, and log analytics.

TLS and mTLS Fundamentals#

TLS (Transport Layer Security) encrypts traffic between two endpoints. Mutual TLS (mTLS) adds a second layer: both sides prove their identity with certificates. Understanding these is not optional for anyone building distributed systems — nearly every production failure involving “connection refused” or “certificate verify failed” traces back to a TLS misconfiguration.

How TLS Works#

A TLS handshake establishes an encrypted channel before any application data is sent. The simplified flow:

Certificate Basics#

A TLS certificate binds a public key to a domain name. The certificate is signed by a Certificate Authority (CA) that browsers and operating systems trust. The chain goes: your certificate, signed by an intermediate CA, signed by a root CA. All three must be present and valid for a client to trust the connection.

Self-Signed Certificates for Development#

For local development and testing, generate a self-signed certificate. Clients will not trust it by default, but you can add it to your local trust store.

Tool Use Patterns#

An agent with access to 30 tools is not automatically more capable than one with 5. What matters is how it selects, sequences, and validates tool use. Poor tool use wastes tokens, introduces latency, and produces wrong results that look right.

Choosing the Right Tool#

When multiple tools could handle a task, the agent must pick the best one. This is harder than it sounds because tool descriptions are imperfect and tasks are ambiguous.

Two-Pass Analysis: Summarize-Then-Correlate#

A 32B model with a 32K context window can process roughly 8-10 source files at once. A real codebase has hundreds. Concatenating everything into one prompt fails — the context overflows, quality degrades, and the model either truncates or hallucinates connections.

The two-pass pattern solves this by splitting analysis into two stages:

1. Pass 1 (Summarize): A fast 7B model reads each file independently and produces a focused summary.
2. Pass 2 (Correlate): A capable 32B model reads all summaries (which are much shorter than the original files) and answers the cross-cutting question.

This effectively multiplies your context window by the compression ratio of summarization — typically 10-20x. A 32K context that handles 10 files directly can handle 100-200 files through summaries.

Upgrading Kubernetes Clusters Safely#

Kubernetes releases a new minor version roughly every four months. Staying current is not optional – clusters more than three versions behind lose security patches, and skipping versions during upgrade is not supported. Every upgrade must step through each minor version sequentially.

Version Skew Policy#

The version skew policy defines which component version combinations are supported:

• kube-apiserver instances within an HA cluster can differ by at most 1 minor version.
• kubelet can be up to 3 minor versions older than kube-apiserver (changed from 2 in Kubernetes 1.28+), but never newer.
• kube-controller-manager, kube-scheduler, and kube-proxy must not be newer than kube-apiserver and can be up to 1 minor version older.
• kubectl is supported within 1 minor version (older or newer) of kube-apiserver.

The practical consequence: always upgrade the control plane first, then node pools. Never upgrade nodes past the control plane version.

Upgrading Self-Managed Kubernetes Clusters with kubeadm#

Upgrading a kubeadm-managed cluster is a multi-step procedure that must be executed in a precise order. The control plane upgrades first, then worker nodes one at a time. Skipping steps or upgrading in the wrong order causes version skew violations that can break cluster communication.

This article provides the complete operational sequence. Execute each step in order. Do not skip ahead.

Version Skew Policy#

Kubernetes enforces strict version compatibility rules between components. Violating these rules results in undefined behavior – sometimes things work, sometimes the API server rejects requests, sometimes components silently fail.

Validation Path Selection#

Not every infrastructure change needs a full Kubernetes cluster to validate. Some changes can be verified with a linter in under a second. Others genuinely need a multi-node cluster with ingress, persistent volumes, and network policies. The cost of choosing wrong is real in both directions: too little validation lets broken configs reach production, while too much wastes minutes or hours on environments you did not need.

Validation Playbook Format#

A validation playbook is a structured procedure that tells an agent exactly how to validate a specific type of infrastructure change. The key problem it solves: the same validation (for example, “verify this Helm chart works”) requires different commands depending on whether the agent has access to kind, minikube, a cloud cluster, or nothing but a linter. A playbook encodes all path variants in one document so the agent picks the right commands for its environment.

Velero Backup and Restore#

Velero backs up Kubernetes resources and persistent volume data to object storage. It handles scheduled backups, on-demand snapshots, and restores to the same or a different cluster. It is the standard tool for Kubernetes disaster recovery.

Velero captures two things: Kubernetes API objects (stored as JSON) and persistent volume data (via cloud volume snapshots or file-level backup with Kopia).

Installation#

You need an object storage bucket (S3, GCS, Azure Blob, or MinIO) and write credentials.