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      "body": "Containers and Python's `uv` package manager should be a perfect match. Fast builds, efficient dependency management, small images… what's not to love? Except when your production containers mysteriously can't import Django, rebuilds take forever despite unchanged dependencies, or when development and production behave completely differently.\n\nI was doing multi-stage builds, separating concerns, following best practices. But I was still hitting these issues constantly. Turns out, I wasn't doing it quite right. Here's what I learned about making Python, uv, and Docker actually work together.\n\nhttps://cdn.hashnode.com/res/hashnode/image/upload/v1766734572055/e31d20ea-e5df-4276-b07f-c6882fa87a7d.png?w=1600&h=840&fit=crop&crop=entropy&auto=compress,format&format=webp\n\n## What I Was Doing Wrong\n\nWhat I need in development isn’t necessarily needed in production. `ruff` - a Python linter and code formatter, for example - has no business in production. It’s purpose is to help a developer write cleaner and maintainable code. Hence the need for multi-stage builds. The right idea, but the execution was the problem.\n\nHere’s a simplified version of my original approach:\n\n```dockerfile\n# Stage 1: Build dependencies\nFROM python:3.12-slim-bookworm AS builder\n\nRUN pip install uv==0.8.15\n\nWORKDIR /build\nCOPY pyproject.toml uv.lock ./\nRUN uv sync --frozen --no-cache --no-dev\n\n# Stage 2: Production runtime\nFROM python:3.12-slim-bookworm AS production\n\nWORKDIR /app\nCOPY --from=builder /build/.venv .venv\n\nENV PATH=\"/app/.venv/bin:$PATH\"\nCOPY . .\n\nCMD [\"python\", \"manage.py\", \"runserver\", \"8000\"]\n```\n\nThis looks reasonable at first glance. I’m using multi-stage builds, separating build from runtime, and only including production dependencies. So what’s the problem?\n\n### Issue #1: Path Mismatch\n\nNotice that the virtual environment is created in `/build.venv` during the builder stage, but my application code lives in `/app` in the production stage. The assumption is that the venv created would live alongside the code in `/build`, but now they’re separated. This can cause import issues and broken paths within the venv, which was the most common problem I experienced.\n\n### Issue #2: Manual uv installation\n\nI’m installing uv via pip in the builder. What this means is that I’m not getting uv’s Docker-specific optimizations. uv has official Docker images that come pre-configured to prevent these exact issues, but I wasn't using them.\n\n### Issue #3: No Layer Caching\n\n```dockerfile\nCOPY pyproject.toml uv.lock ./\nRUN uv sync --frozen --no-cache --no-dev\n```\n\nThe builder stage uses `--no-cache` and doesn't leverage BuildKit cache mounts, so package downloads aren't cached between builds. Even unchanged dependencies get re-downloaded whenever `pyproject.toml` or `uv.lock` changes, making rebuilds unnecessarily slow.\n\n### Issue #4: Symlinks Breaking Across Stages\n\nBy default, uv creates symlinks when installing packages… because it’s faster. But the problem with that in this type of situation is that symlinks created in the builder stage can point to locations that don’t exist when copied to the production stage. What then happens is something like, “Cannot import Django” — import errors that only up at runtime.\n\nLet me show you what happens when I try to add a development stage:\n\n```dockerfile\n# ... builder stage ...\n\nFROM python:3.12-slim-bookworm AS development\n\nWORKDIR /app\nCOPY --from=builder /build/.venv /build/.venv\nENV PATH=\"/build/.venv/bin:$PATH\"\n\n# Now install dev dependencies\nCOPY pyproject.toml uv.lock ./\nRUN pip install uv==0.8.15  # Installing uv AGAIN (wasteful)\nRUN uv sync --frozen --no-cache --group dev\n\nCOPY . .\nCMD [\"python\", \"manage.py\", \"runserver\", \"8000\"]\n```\n\nNow I have multiple problems compounding:\n\n* The base production venv is in `/build/.venv`\n    \n* I'm trying to add dev dependencies to it, but my working directory is `/app`\n    \n* I'm installing uv again (wasteful)\n    \n* The paths are all over the place\n    \n\nThis all results to inconsistent behaviors. While I managed to remedy the situation somehow and got containers run just fine eventually, I was losing out on more efficient approaches.\n\n## The Right Approach\n\nThere had to be a better way, I figured after running into the aforementioned issues in a new project. I felt I didn’t know enough, having ran into the same issues again. After digging through uv’s documentation and examples, I discovered the patterns that actually work reliably.\n\n```dockerfile\n# Stage 1: Build with official uv image\nFROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim AS builder\n\n# Critical uv settings for Docker\nENV UV_COMPILE_BYTECODE=1 \\\n    UV_LINK_MODE=copy \\\n    UV_PYTHON_DOWNLOADS=0\n\nWORKDIR /app\n\n# Install dependencies with cache mounts\nRUN --mount=type=cache,target=/root/.cache/uv \\\n    --mount=type=bind,source=uv.lock,target=uv.lock \\\n    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \\\n    uv sync --locked --no-install-project --no-dev\n\n# Copy source and install project\nCOPY . /app\nRUN --mount=type=cache,target=/root/.cache/uv \\\n    uv sync --locked --no-dev\n\n# Stage 2: Minimal production runtime\nFROM python:3.12-slim-bookworm AS production\n\n# Create non-root user\nRUN groupadd --system --gid 999 appuser \\\n && useradd --system --gid 999 --uid 999 --create-home appuser\n\n# Copy the entire app including venv - paths stay consistent\nCOPY --from=builder --chown=appuser:appuser /app /app\n\nENV PATH=\"/app/.venv/bin:$PATH\"\nUSER appuser\nWORKDIR /app\n\nCMD [\"python\", \"manage.py\", \"runserver\", \"8000\"]\n```\n\nLet's break down what changed and why it works:\n\n### Fix #1: Using the Official uv Image\n\n```dockerfile\nFROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim AS builder\n```\n\nIt turned out that the Astral team - makers of uv and ruff - specifically designed a base image for Docker workflows. It comes with uv pre-installed and properly configured for container environments.\n\n### Fix #2: The Magic Environment Variables\n\n```dockerfile\nENV UV_COMPILE_BYTECODE=1 \\\n    UV_LINK_MODE=copy \\\n    UV_PYTHON_DOWNLOADS=0\n```\n\nYou see these three settings? They’re crucial:\n\n* `UV_COMPILE_BYTECODE=1` - Compiles Python bytecode during the build, not at runtime. Containers start faster because Python doesn't need to compile `.py` files to `.pyc` on first import.\n    \n* `UV_LINK_MODE=copy` - Makes uv copy files instead of creating symlinks. The venv becomes truly self-contained and portable across build stages.\n    \n* `UV_PYTHON_DOWNLOADS=0` - Tells uv to use the system Python interpreter instead of downloading its own. Builder and production stages use the exact same Python, no subtle compatibility issues.\n    \n\n### Fix #3: BuildKit Cache Mounts\n\n```dockerfile\nRUN --mount=type=cache,target=/root/.cache/uv \\\n    --mount=type=bind,source=uv.lock,target=uv.lock \\\n    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \\\n    uv sync --locked --no-install-project --no-dev\n```\n\nIt’s at this point we talk about speed improvements:\n\n* `--mount=type=cache` persists uv’s download cache between builds. This means that when I change one dependency, only that package gets re-downloaded, not the entire dependency tree.\n    \n* `--mount=type=bind` temporarily mounts only the files needed for dependency resolution. Docker can detect when these specific files change and invalidate the cache appropriately.\n    \n* `--no-install-project` first installs just dependencies (which changes rarely) then copies source code in a separate step and installs the project itself (which changes frequently). Better layer caching means faster rebulids.\n    \n\n### Fix #4: Path Consistency\n\n```dockerfile\n# Builder\nWORKDIR /app\nCOPY . /app\nRUN uv sync --locked --no-dev\n\n# Production\nCOPY --from=builder --chown=appuser:appuser /app /app\nWORKDIR /app\n```\n\nEverything happens in `/app`. the venv is created in `/app/.venv`, the source code lives right in `/app`, and the production stage runs from `/app` as well. No path confusion or broken references this time.\n\n### Adding Development Stage (The Right Way)\n\n```dockerfile\nFROM base AS development\n\n# Use build args to conditionally install dev dependencies\nFROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim AS builder-dev\n\nENV UV_COMPILE_BYTECODE=1 \\\n    UV_LINK_MODE=copy \\\n    UV_PYTHON_DOWNLOADS=0\n\nWORKDIR /app\n\nRUN --mount=type=cache,target=/root/.cache/uv \\\n    --mount=type=bind,source=uv.lock,target=uv.lock \\\n    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \\\n    uv sync --locked --no-install-project --group dev\n\nCOPY . /app\nRUN --mount=type=cache,target=/root/.cache/uv \\\n    uv sync --locked --group dev\n\nFROM python:3.12-slim-bookworm AS development\n\nRUN groupadd --system --gid 999 appuser \\\n && useradd --system --gid 999 --uid 999 --create-home appuser\n\nCOPY --from=builder-dev --chown=appuser:appuser /app /app\n\nENV PATH=\"/app/.venv/bin:$PATH\"\nUSER appuser\nWORKDIR /app\n\nCMD [\"python\", \"manage.py\", \"runserver\", \"8000\"]\n```\n\nThis makes more sense. Same consistent paths, same pattern, just including dev dependency group. No more reinstalling uv, no path kung-fu.\n\n## The Results\n\n**Before vs. After:**\n\n| Metric | Before | After |\n| --- | --- | --- |\n| First build | ~5 minutes | ~4 minutes |\n| Rebuild (dep change) | 3-5 minutes | 10-20 seconds |\n| Rebuild (code change) | 2-3 minutes | 5-10 seconds |\n| Production image | ~300MB | ~150MB |\n| Import errors | Frequent | None |\n\n**What this means in practice:**\n\n* **Development velocity** - Code changes rebuild in seconds, not minutes\n    \n* **CI/CD efficiency** - Tests run faster with cached dependencies\n    \n* **Deployment confidence** - Same paths everywhere = no surprises in production\n    \n* **Resource efficiency** - Smaller images = faster pulls, lower bandwidth costs\n    \n\nThis is exactly what we want. Predictability and efficiency.\n\n## Production Considerations:\n\nThe simplified examples above demonstrate the core concepts, but production environments need more:\n\n* **Security hardening** - Non-root users, gosu for proper signal handling, GPG verification\n    \n* **Multiple build stages** - Separate dev, test, staging, and production\n    \n* **Conditional dependencies** - Build args to optionally include dev/test/docs groups\n    \n* **Full Django stack** - Including Celery workers, beat scheduler, migrations\n    \n* **Observability** - Health checks, resource limits, OpenTelemetry integration\n    \n\nI'll share in future posts how I actually do this in production for my Python (Django/FastAPI) projects.\n\n---\n\n<sub>Originally published at [blog.theolujay.dev](https://blog.theolujay.dev/multi-stage-docker-uv-python)</sub>",
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      "body": "At 2:23 AM last Tuesday, something went wrong with our database. Backups were a few days old, and so recovery took longer. That’s when I realized our backup system had to be better.\n\nhttps://cdn.hashnode.com/res/hashnode/image/upload/v1765867594016/6a876012-b7bd-40de-a696-d3908ccf69ab.png?w=1600&h=840&fit=crop&crop=entropy&auto=compress,format&format=webp\n\nManaged databases can be expensive to run. Going self-managed is the alternative, but it comes with its own responsibilities to achieve resiliency. Part of that for me involves building a backup system that:\n\n* Runs automatically at 2 AM\n    \n* Retries up to 10 times if something fails\n    \n* Sends Slack notifications on success or failure\n    \n* Stores backups in S3\n    \n* Makes restoration a single command\n    \n\nNo database was harmed in the making of this system. Here's how I did it.\n\n## The Stack\n\n* **Database:** PostgreSQL 17 in Docker\n    \n* **Storage:** AWS S3 with lifecycle policies\n    \n* **Orchestration:** Bash scripts + cron\n    \n* **Notifications:** Slack webhooks\n    \n* **Backup tool:** pg\\_dump (PostgreSQL's built-in tool)\n    \n\n## Architecture Overview\n\nThe backup system has three main components:\n\n1. **The Backup Script** - Handles the actual `pg_dump`, S3 upload, and local cleanup. Includes retry logic for resilience.\n    \n2. **Cron Jobs** - Schedules backups (production daily, staging weekly).\n    \n3. **Notification Webhook** - POSTs status updates directly to Slack webhook.\n    \n\nThis is straightforward… cron triggers the script → script backs up database → uploads to S3 → sends notification → cleans up old local files\n\n## Implementation\n\n### **Step 1: Configure AWS and Docker**\n\n**AWS Setup**:\n\nFirst, you want configure AWS CLI on your host machine and create S3 buckets for your backups. [Install AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html) if it’s not.\n\n```bash\naws configure\n# Enter your credentials and region\n\n# Test access\naws s3 ls\n```\n\n**Docker Setup:**\n\nAdd a volume mount to your database service in `compose.yml`:\n\n```yaml\nservices:\n  db:\n    image: postgres:17.2-bookworm\n    volumes:\n      - db_data:/var/lib/postgresql/data\n      - ./backups:/backups  # Add this for backup files\n    environment:\n      POSTGRES_DB: myapp_db\n      POSTGRES_USER: myapp_user\n      POSTGRES_PASSWORD: ${DB_PASSWORD}\nvolumes:\n  db_data:\n```\n\nRestart your containers to apply the changes:\n\n```bash\ndocker compose up -d\n```\n\n> **Production Note:** I actually use Docker Swarm in production/staging for better orchestration. The backup strategy is pretty much the same. Just update container filters to `docker ps -qf \"name=stackname_db.1\"` instead of `\"name=projectname_db\"`. I'll cover this in the Production Considerations section.\n\n### **Step 2: The Backup Script**\n\nCreate `~/scripts/backup.sh` with three key functions:\n\n1. `run_backup()` - Executes `pg_dump` via `docker exec`, uploads to S3, cleans up old local files\n    \n2. `send_notification()` - POSTs backup status directly to Slack\n    \n3. Retry logic - attempts backup up to 10 times with 10-minute intervals\n    \n\nHere's the simplified structure:\n\n```bash\n#!/bin/bash\nset -euo pipefail\n\nENV=\"${1:-prod}\"\nMAX_RETRIES=10\nRETRY_INTERVAL=600  # 10 minutes\n\n# load configs (Slack webhook, AWS region, etc.)\nsource ~/.backup_env\n\n# environment-specific config\nif [ \"$ENV\" == \"prod\" ]; then\n    DB_NAME=\"myapp_prod\"\n    DB_USER=\"myapp_user\"\n    S3_BUCKET=\"myapp-backups\"\nelse\n    DB_NAME=\"myapp_staging\"\n    DB_USER=\"myapp_user\"\n    S3_BUCKET=\"myapp-staging-backups\"\nfi\n\nBACKUP_FILE=\"backup_$(date +%Y-%m-%dT%H-%M-%S).dump\"\n\nsend_notification() {\n    local status=$1\n    local error_msg=${2:-\"\"}\n    \n    # determine color\n    local color=\"good\"\n    [[ \"$status\" == *\"failure\"* ]] && color=\"danger\"\n    \n    # build the Slack payload\n    local payload=$(cat <<EOF\n{\n  \"text\": \"DB Backup for ${ENV}: ${status}\",\n  \"attachments\": [{\n    \"color\": \"${color}\",\n    \"fields\": [\n      {\"title\": \"Environment\", \"value\": \"${ENV}\", \"short\": true},\n      {\"title\": \"Status\", \"value\": \"${status}\", \"short\": true},\n      {\"title\": \"Backup File\", \"value\": \"${BACKUP_FILE}\", \"short\": false}\n      $([ -n \"$error_msg\" ] && echo \", {\\\"title\\\": \\\"Error\\\", \\\"value\\\": \\\"${error_msg}\\\", \\\"short\\\": false}\")\n    ]\n  }]\n}\nEOF\n)\n    \n    # send to Slack\n    curl -X POST \"$SLACK_WEBHOOK_URL\" \\\n        -H \"Content-Type: application/json\" \\\n        -d \"$payload\" --silent --show-error\n}\n\nrun_backup() {\n    # find database container\n    local container_id=$(docker ps -qf \"name=db\")\n    \n    # run pg_dump inside container\n    docker exec \"$container_id\" \\\n        pg_dump -U $DB_USER -d $DB_NAME \\\n        -Fc -b -v -f /backups/$BACKUP_FILE\n    \n    # Upload to S3\n    aws s3 cp ~/backups/$BACKUP_FILE \\\n        s3://$S3_BUCKET/ --region us-east-1\n    \n    # Keep only last 2 local backups\n    ls -t ~/backups/*.dump | tail -n +3 | xargs -r rm\n}\n\n# Main execution with retry logic\nif run_backup; then\n    send_notification \"success\"\n    exit 0\nelse\n    send_notification \"first_failure\" \"Initial backup failed\"\n    \n    for i in $(seq 1 $MAX_RETRIES); do\n        sleep $RETRY_INTERVAL\n        if run_backup; then\n            send_notification \"success_after_retry\"\n            exit 0\n        fi\n    done\n    \n    send_notification \"final_failure\" \"All retries exhausted\"\n    exit 1\nfi\n```\n\nBreaking down the `docker exec` command with `pg_dump`:\n\n```bash\ndocker exec -d 42f3acbe6d70 pg_dump -U $DB_USER -d $DB_NAME -Fc -b -v -f /backups/$BACKUP_FILE\n│      │     │ │            │        │           │           ││  │  │  | │\n│      │     │ │            │        │           │           ││  │  │  | └─ Output file path with variable.\n│      │     │ │            │        │           │           ││  │  │  └─── File name flag.\n│      │     │ │            │        │           │           ││  │  └────── Specifies verbose mode.\n│      │     │ │            │        │           │           ││  └───────── Include large objects in the dump.\n│      │     │ │            │        │           │           │└──────────── Output a custom-format archive suitable for input into pg_restore\n│      │     │ │            │        │           │           └───────────── Selects the format of the output.\n│      │     │ │            │        │           └───────────────────────── Specifies the name of the database to connect to. \n│      │     │ │            │        └───────────────────────────────────── User name to connect as.\n│      │     │ │            └────────────────────────────────────────────── Export a PostgreSQL database as an SQL script or to other formats\n│      │     │ └─────────────────────────────────────────────────────────── Container ID \n│      │     └───────────────────────────────────────────────────────────── Detached mode: run command in the background\n└──────└─────────────────────────────────────────────────────────────────── Execute a command in a running container\n```\n\n**Key decisions explained:**\n\n* **Why** `-Fc` format? Custom format is compressed and allows selective restoration of specific tables.\n    \n* **Why retry logic?** Networks fail. AWS can hiccup. Retries make the system resilient.\n    \n* **Why keep 2 local backups?** Quick access for recent restores without hitting S3, but doesn't fill the disk.\n    \n* **Why direct Slack posting?** Fewer dependencies. If you already have a notification service, you could POST there instead and let it handle formatting. [Setting up a Slack webhook](https://docs.slack.dev/messaging/sending-messages-using-incoming-webhooks/). What it looks like on Slack:\n    \n    ![](https://cdn.hashnode.com/res/hashnode/image/upload/v1765859755306/2303c827-e5b9-44b3-ad3c-03906facdc96.png align=\"center\")\n    \n\nMake the script executable:\n\n```bash\nchmod +x ~/scripts/backup.sh\n```\n\nCreate the environment file with your secrets:\n\n```bash\ncat > ~/.backup_env << 'EOF'\nSLACK_WEBHOOK_URL=\"https://hooks.slack.com/services/YOUR/WEBHOOK/URL\"\nEOF\n\nchmod 600 ~/.backup_env # only the file's owner has read and write access\n```\n\n### Step 3: Automation with Cron\n\nSchedule the backups to run automatically:\n\n```bash\ncrontab -e\n\n# add these lines:\n# Production - daily at 2 AM\n0 2 * * * /home/username/scripts/backup.sh prod >> /home/username/.db_backup_logs/backup-prod.log 2>&1\n\n# Staging - Sundays at 2 AM  \n0 2 * * 0 /home/username/scripts/backup.sh staging >> /home/username/.db_backup_logs/backup-staging.log 2>&1\n\n# Cleanup old logs - daily at 3 AM\n0 3 * * * find /home/username/.db_backup_logs -name \"*.log\" -mtime +30 -delete\n```\n\n```bash\nmkdir -p ~/.db_backup_logs\n```\n\n### Step 4: The Restoration Script\n\nBackups are useless if you can't restore them. You could do it manually, but why not just use Bash scripting as well? Create `~/scripts/db-restore.sh`:\n\n```bash\n#!/bin/bash\nset -euo pipefail\n\n# Usage: ./db-restore.sh prod backup_2025-12-13T02-00-00.dump [--full]\n\nENV=\"${1}\"\nBACKUP_FILE=\"${2}\"\nFULL_RESTORE=\"${3:-}\"\n\n# Download from S3\naws s3 cp s3://myapp-backups/$BACKUP_FILE ~/restore/\n\n# Copy to container\nCONTAINER_ID=$(docker ps -qf \"name=db\")\ndocker cp ~/restore/$BACKUP_FILE $CONTAINER_ID:/tmp/\n\n# Restore (with confirmation prompt)\nif [ \"$FULL_RESTORE\" == \"--full\" ]; then\n    # Drop and recreate database\n    docker exec -i $CONTAINER_ID psql -U postgres <<EOF\nDROP DATABASE IF EXISTS myapp_prod;\nCREATE DATABASE myapp_prod;\nEOF\nfi\n\n# Run pg_restore\ndocker exec $CONTAINER_ID \\\n    pg_restore -U myapp_user -d myapp_prod \\\n    --clean --if-exists -v /tmp/$BACKUP_FILE\n\n# Cleanup\ndocker exec $CONTAINER_ID rm /tmp/$BACKUP_FILE\nrm ~/restore/$BACKUP_FILE\n```\n\nMake it executable:\n\n```bash\nchmod +x ~/scripts/db-restore.sh\n```\n\n**Testing restoration is very important.** You might want to do this regularly, perhaps monthly.\n\n```bash\n# Download latest prod backup\nLATEST=$(aws s3 ls s3://myapp-backups/ | sort | tail -n 1 | awk '{print $4}')\n\n# Restore to staging\n./scripts/db-restore.sh staging $LATEST --full\n```\n\n## Production Considerations\n\n### Docker Swarm vs Compose\n\nIn production, I use Docker Swarm instead of Compose for better orchestration and secrets management. It’s actually the same backup strategy with a few changes:\n\n**Container naming:**\n\n* Compose: `projectname_db_1`\n    \n* Stack: `stackname_db.1.container_id`\n    \n\n**Finding containers:**\n\n```bash\n# works for both\ndocker ps -qf \"name=db\"\n```\n\n**Secrets management:**\n\nDocker Swarm handles secrets more securely:\n\n```yaml\nservices:\n  db:\n    secrets:\n      - db_password\n    environment:\n      POSTGRES_PASSWORD_FILE: /run/secrets/db_password\n\nsecrets:\n  db_password:\n    external: true\n```\n\nCreate the secret:\n\n```bash\n# I find stdin to be easier to work with\nprintf \"your-database-password-here\" | docker secret create db-password -\n```\n\nThen in the backup script, read the password from inside the container:\n\n```bash\ndocker exec $CONTAINER_ID sh -c \\\n    \"PGPASSWORD=\\$(cat /run/secrets/db_password) pg_dump ...\"\n```\n\n### S3 Lifecycle Policies\n\nIf you want to prevent backup costs from growing forever, consider auto-deleting old backups:\n\n1. Go to S3 Console → Your bucket → Management → Lifecycle rules\n    \n2. Create rule: Delete objects older than 30 days\n    \n3. Apply to prefix: `prod-db-backups/`\n    \n\n### Monitoring\n\nBeyond Slack notifications, you could also:\n\n* Review logs: `tail -50 ~/.db_backup_logs/backup-prod.log`\n    \n* Check S3: `aws s3 ls s3://myapp-backups/ | tail`\n    \n* Test restoration at intervals (as mentioned above)\n    \n\n## The Result\n\n* Automated nightly backups\n    \n* Slack notifications keeping us notified\n    \n* Testing restoration process\n    \n* Off-site storage in S3\n    \n* and of course, peace of mind, heh…\n    \n\nS3 buckets are way cheaper than RDS instances, so this to me is part of keeping costs low for projects without sacrificing the safety net of reliable backups.\n\n## Try It Yourself\n\nFull scripts available in this [GitHub Gist](https://gist.github.com/theolujay/b7131a0d6553299507124c46fadfd4a7). The system is environment-agnostic. Only update the configuration variables for your setup.\n\n**Questions? Drop them in the comments.**\n\n---\n\n<sub>Originally published at [blog.theolujay.dev](https://blog.theolujay.dev/pg-backup-docker-s3-slack)</sub>",
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      "body": "Congratulations @theolujay! You have completed the following achievement on the Hive blockchain And have been rewarded with New badge(s)\n\n<table><tr><td><img src=\"https://images.hive.blog/60x70/https://hivebuzz.me/@theolujay/upvoted.png?202512311824\"></td><td>You received more than 10 upvotes.<br>Your next target is to reach 50 upvotes.</td></tr>\n</table>\n\n<sub>_You can view your badges on [your board](https://hivebuzz.me/@theolujay) and compare yourself to others in the [Ranking](https://hivebuzz.me/ranking)_</sub>\n<sub>_If you no longer want to receive notifications, reply to this comment with the word_ `STOP`</sub>\n\n\n\n**Check out our last posts:**\n<table><tr><td><a href=\"/hive-122221/@hivebuzz/pum-202601\"><img src=\"https://images.hive.blog/64x128/https://i.imgur.com/M9RD8KS.png\"></a></td><td><a href=\"/hive-122221/@hivebuzz/pum-202601\">Be ready for the January edition of the Hive Power Up Month!</a></td></tr><tr><td><a href=\"/hive-122221/@hivebuzz/pud-202601\"><img src=\"https://images.hive.blog/64x128/https://i.imgur.com/805FIIt.jpg\"></a></td><td><a href=\"/hive-122221/@hivebuzz/pud-202601\">Hive Power Up Day - January 1st 2026</a></td></tr></table>",
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      "body": "This blog might have been inaccessible to you if you’re not in the US or UK or using a VPN. Since I plugged in my own domain to Hashnode, I almost never could visit my own page without a VPN. I knew I followed the setup instructions right, so after feeling frustrated for so long, I concluded that Hashnode was down or something… yet I resisted defeat, went down a rabbit hole, and found something fascinating.\n\nhttps://cdn.hashnode.com/res/hashnode/image/upload/v1764093484839/1312f52a-3175-4abb-a4fa-165c0f926d23.png\n\nI’ve been learning about DNS and cloud technologies lately. Problems that arose at work have me figuring out things I might never have outside the job. And so when the internet downtimes we experienced lately occured - think AWS and Cloudflare - I could fairly understand the explanations for what happened. Even at that, I didn’t quite realize how big of a deal Cloudflare is today until they turned out to be the answer to the problems with my blog.\n\nHashnode was apparently serving my blog from Vercel, but the DNS path from where I’m from, Nigeria, to that Vercel edge location was basically trash. I mean, it somehow wasn’t working. So even when I’d curl https://blog.theolujay.dev, it would just stall and nothing would happen. Perhaps it was my ISP, but I figured that whatever traffic route it was taking wasn’t cutting it. The VPN “fixed” it because, you know, it teleported my traffic to a different location, which was usually the US, where Vercel’s edge was fine.\n\nI surely couldn’t write to Hashnode or my ISP to move the world for me, just because I was starting to write again. Down the hole I began to dig to find answers, I realized that Cloudflare is perfectly positioned for this type of problem. And that’s how I started to read and learn more about them and this whole [“Connectivity Cloud” concept](https://blog.cloudflare.com/welcome-to-connectivity-cloud/). Prior to this situation of mine, I had already been inspired by their explanation of the [Cloudflare outage on November 18, 2025](https://blog.cloudflare.com/18-november-2025-outage/).\n\nhttps://cdn.hashnode.com/res/hashnode/image/upload/v1764092979473/c7861635-227b-420b-8b53-ae068936f212.png\n\nNamecheap is my domain registrar. I was using their DNS system, which wasn’t helping me out. I signed up on Cloudflare, got new nameservers from them, and added them to my domain settings on Namecheap. After it took a while to activate, my blog was now accessible without VPN. It really was as easy as that.\n\nIn other words, DNS queries for my domain now hit Cloudflare’s Anycast network (a nearby data center) and pull content from Hashnode/Vercel over their network. And then my stuff is accessible from Cloudflare’s edge. So apparently, Hashnode wasn’t the problem. It was a routing failure between where I am and their infrastructure.\n\n---\n\n<sub>Originally published at [blog.theolujay.dev](https://blog.theolujay.dev/how-cloudflare-fixed-my-geo-locked-blog)</sub>",
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      "body": "99.99% efficiency sounds great on paper. In practice you’re usually one better tool or one small habit away from not wasting an hour on installs, CI runs, or repetitive terminal typing. Here are three practical changes I use that save time every day.\n\n## 1) Use `uv` instead of raw `pip`/`venv`\n\n`uv` is a modern, Rust-powered package & project manager that combines `virtualenv`, `pip`, and `pipx`, and it’s really fast. On projects with many dependencies, you’ll see massive wins with it.\n\nHere’s a quick benchmark takeaway: on one of my projects `pip install -r requirements.txt` took ~150s while `uv pip install` completed in ~1s. It’s needless to say you start projects quicker and your app builds are significantly faster.\n\n**Install uv**\n\n* macOS / Linux (installer script):\n    \n\n```bash\ncurl -LsSf https://astral.sh/uv/install.sh | sh\n```\n\n* Homebrew:\n    \n\n```bash\nbrew install uv\n```\n\n* Windows (PowerShell installer / winget / scoop) — see official docs for options and latest instructions. (\\[Astral Docs\\]\\[2\\])\n    \n\n**Useful \\`uv\\` commands**\n\n```bash\n# create venv (uses .venv by default)\nuv venv\n\n# install packages from pyproject or requirements via uv\nuv pip install -r requirements.txt\n\n# install only non-dev groups from pyproject\nuv sync --no-dev\n\n# add packages to groups\nuv add --group dev --group test\n```\n\nI can then run a command like the one below to install just the main packages.\n\n```plaintext\nuv sync --no-dev\n```\n\nOr if I wanted specific (or all) groups, I could do that too.\n\n```plaintext\nuv add --group dev --group test\n```\n\nThere are many more interesting ways to use `uv`, like running some commands faster, but that’ll be in a future post.\n\n## 2) Bash aliases\n\nStop retyping long commands. You could put frequently used aliases in `~/.bash_aliases` (or `~/.bashrc`) and use them instead.\n\nPractical examples I use\n\n```bash\nalias ptree='tree -I \"__pycache__|*.pyc|.venv|.git|node_modules\" --dirsfirst -L 3'\nalias py='python'\nalias pyma='python3 manage.py'\nalias entervenv='source .venv/bin/activate'\nalias dc='docker compose'\nalias gcm='git commit -m'\n```\n\n## 3) Small scripts &gt; manual workflows\n\nUpdating secrets in a running Swarm stack, for example, is a multi-step, error-prone process. Although it’s not something you’d frequently do, it’s all the same a tedious process. Wrap it in a script so you don’t do it wrong at 3 AM.\n\n```bash\n#!/bin/bash\n\n# Usage: ./update-secret.sh secret-name secret-value [stack-name]\n\nset -euo pipefail\n\nSECRET_NAME=$1\nSECRET_VALUE=$2\nSTACK_NAME=${3:-vmlc-prod}\n\nif [ -z \"$SECRET_NAME\" ] || [ -z \"$SECRET_VALUE\" ]; then\n    echo \"Usage: $0 <secret_name> <secret_value> [stack_name]\"\n    exit 1\nfi\n\nTEMP_SECRET_NAME=\"${SECRET_NAME}-temp\"\n\nprintf \"%s\" \"$SECRET_VALUE\" | docker secret create \"$TEMP_SECRET_NAME\" -\n\nfor service in db-migrate django celery_beat celery_worker; do\n    docker service update \\\n      --secret-rm \"$SECRET_NAME\" \\\n      --secret-add \"source=$TEMP_SECRET_NAME,target=$SECRET_NAME\" \\\n      \"${STACK_NAME}_${service}\" > /dev/null\ndone\n\nsleep 10\ndocker secret rm \"$SECRET_NAME\"\nprintf \"%s\" \"$SECRET_VALUE\" | docker secret create \"$SECRET_NAME\" -\n\nfor service in db-migrate django celery_beat celery_worker; do\n    docker service update \\\n      --secret-rm \"$TEMP_SECRET_NAME\" \\\n      --secret-add \"source=$SECRET_NAME,target=$SECRET_NAME\" \\\n      \"${STACK_NAME}_${service}\" > /dev/null\ndone\n\nsleep 10\ndocker secret rm \"$TEMP_SECRET_NAME\"\necho \"Done! Secret $SECRET_NAME updated successfully.\"\n```\n\nIf you copy this, adapt the \\`service\\` list to match your stack and test on staging first.\n\n### Bonus: prompt info at-a-glance\n\nAdd PS1 bits so your shell prompt shows whether you’re in a venv, the current git branch, and Docker context. There are various ways to customize it.\n\n![](https://cdn.hashnode.com/res/hashnode/image/upload/v1763918195391/bccc3817-0cbd-4da5-9881-c06e0f0f22bc.png align=\"center\")\n\nFrom the image above, you can see that I have `(.venv)` that shows up when I’m in a virtual environment, `[release]` to show the `git branch` I’m in, and `{default}` to indicate the Docker Context I’m in. They’re colored differently so I can quickly differentiate them. The `$` is colored differently, too, purely just for style. You can do the same by editing the `PS1` environment variable in `~/.bashrc`. Here’s what mine looks like:\n\n```bash\nif [ \"$color_prompt\" = yes ]; then\n    PS1='$(git rev-parse --abbrev-ref HEAD 2>/dev/null | sed \"s/^/\\[\\033[31m\\][/;s/$/]\\[\\033[00m\\]/\")$(docker context show 2>/dev/null | sed \"s/^/\\[\\033[38;2;0;127;255m\\]{/;s/$/}\\[\\033[00m\\]/\")\\n\\[\\033[01;32m\\]{\\u}\\[\\033[00m\\]\\n\\[\\033[38;5;214m\\]$ \\[\\033[00m\\]'\nelse\n    PS1='${debian_chroot:+($debian_chroot)}\\u@\\h:\\w\\$ '\nfi\n```\n\n### Links / further reading\n\n* Official `uv` docs & installation: [https://docs.astral.sh/uv/getting-started/installation/](https://docs.astral.sh/uv/getting-started/installation/)\n    \n* `uv` overview & features (docs/home): https://docs.astral.sh/uv/\n    \n* RealPython: Managing projects with `uv` (tutorial): [https://realpython.com/python-uv/](https://realpython.com/python-uv/)\n    \n* GitHub repo (source & CLI reference): [https://github.com/astral-sh/uv](https://github.com/astral-sh/uv)\n    \n* Bash basics: [https://github.com/denysdovhan/bash-handbook](https://github.com/denysdovhan/bash-handbook)\n\n---\n<sub>Originally posted at [blog.theolujay.dev](https://theolujay.hashnode.dev/practical-habits-for-productive-python-development)</sub>",
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