跳到正文
cccskills
免费在 GitHub 公开

azure-cosmos-db

Use when working with Azure Cosmos Db — azure Cosmos DB throughput analysis, partition key distribution, consistency levels, metrics monitoring, and database management via Azure CLI.

中文简介正在准备中。当前显示原文说明。

集成工具Azure
查看安装方法

包含的文件(1)

  • SKILL.md7.6 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Cosmos DB Skill

Manage and analyze Azure Cosmos DB accounts using az cosmosdb commands.

Discovery-First Rule

ALWAYS discover before acting. Never assume account names, database names, container names, or throughput values.

# Discover Cosmos DB accounts
az cosmosdb list --output json \
  --query "[].{name:name, rg:resourceGroup, kind:kind, apiKind:databaseAccountOfferType, locations:locations[].locationName, consistencyPolicy:consistencyPolicy.defaultConsistencyLevel}"

Parallel Execution Requirement

ALL independent operations MUST run in parallel using background jobs (&) and wait.

for acct in $(echo "$accounts" | jq -c '.[]'); do
  {
    name=$(echo "$acct" | jq -r '.name')
    rg=$(echo "$acct" | jq -r '.rg')
    az cosmosdb sql database list --account-name "$name" --resource-group "$rg" --output json
  } &
done
wait

Helper Functions

# List SQL databases in an account
list_databases() {
  local acct="$1" rg="$2"
  az cosmosdb sql database list --account-name "$acct" --resource-group "$rg" --output json \
    --query "[].{name:name, throughput:options.throughput, autoscaleMax:options.autoscaleMaxThroughput}"
}

# List containers in a database
list_containers() {
  local acct="$1" rg="$2" db="$3"
  az cosmosdb sql container list --account-name "$acct" --resource-group "$rg" --database-name "$db" --output json \
    --query "[].{name:name, partitionKey:resource.partitionKey.paths[0], indexingMode:resource.indexingPolicy.indexingMode, ttl:resource.defaultTtl}"
}

# Get container throughput
get_throughput() {
  local acct="$1" rg="$2" db="$3" container="$4"
  az cosmosdb sql container throughput show --account-name "$acct" --resource-group "$rg" \
    --database-name "$db" --name "$container" --output json \
    --query "{throughput:resource.throughput, autoscaleMax:resource.autoscaleMaximumThroughput, minimumThroughput:resource.minimumThroughput}"
}

# Get Cosmos DB metrics
get_cosmos_metrics() {
  local resource_id="$1" metric="$2"
  az monitor metrics list --resource "$resource_id" --metric "$metric" \
    --interval PT1H --aggregation Average Total --output json
}

Common Operations

1. Account and Database Overview

accounts=$(az cosmosdb list --output json --query "[].{name:name, rg:resourceGroup}")
for acct in $(echo "$accounts" | jq -c '.[]'); do
  {
    name=$(echo "$acct" | jq -r '.name')
    rg=$(echo "$acct" | jq -r '.rg')
    echo "=== Account: $name ==="
    az cosmosdb show --name "$name" --resource-group "$rg" --output json \
      --query "{consistency:consistencyPolicy.defaultConsistencyLevel, multiWrite:enableMultipleWriteLocations, locations:locations[].{loc:locationName, failoverPriority:failoverPriority}, backupPolicy:backupPolicy.type}"
    list_databases "$name" "$rg"
  } &
done
wait

2. Throughput Analysis

# Check RU consumption vs provisioned throughput
resource_id=$(az cosmosdb show --name "$ACCT" --resource-group "$RG" --query "id" -o tsv)
az monitor metrics list --resource "$resource_id" \
  --metric "TotalRequestUnits" "NormalizedRUConsumption" "ProvisionedThroughput" \
  --interval PT1H --aggregation Average Total Max --output json

# Check for 429 (throttled) requests
az monitor metrics list --resource "$resource_id" \
  --metric "TotalRequests" --interval PT1H --aggregation Count \
  --filter "StatusCode eq '429'" --output json

3. Partition Key Distribution

# Check partition key ranges and usage
az cosmosdb sql container show --account-name "$ACCT" --resource-group "$RG" \
  --database-name "$DB" --name "$CONTAINER" --output json \
  --query "{partitionKey:resource.partitionKey, conflictResolution:resource.conflictResolutionPolicy}"

# Get partition-level metrics (hot partitions)
az monitor metrics list --resource "$resource_id" \
  --metric "NormalizedRUConsumption" --interval PT5M --aggregation Max \
  --dimension "PartitionKeyRangeId" --output json

4. Consistency Level Review

# Check account-level consistency
az cosmosdb show --name "$ACCT" --resource-group "$RG" --output json \
  --query "{defaultConsistency:consistencyPolicy.defaultConsistencyLevel, maxStaleness:consistencyPolicy.maxStalenessPrefix, maxInterval:consistencyPolicy.maxIntervalInSeconds}"

5. Backup and Replication Status

az cosmosdb show --name "$ACCT" --resource-group "$RG" --output json \
  --query "{backupPolicy:backupPolicy, locations:locations[].{name:locationName, priority:failoverPriority, isZoneRedundant:isZoneRedundant}, enableAutomaticFailover:enableAutomaticFailover}"

Output Format

Present results as a structured report:

Azure Cosmos Db Report
══════════════════════
Resources discovered: [count]

Resource       Status    Key Metric    Issues
──────────────────────────────────────────────
[name]         [ok/warn] [value]       [findings]

Summary: [total] resources | [ok] healthy | [warn] warnings | [crit] critical
Action Items: [list of prioritized findings]

Target ≤50 lines of output. Use tables for multi-resource comparisons.

Anti-Hallucination Rules

  1. NEVER assume resource names — always discover via CLI/API in Phase 1 before referencing in Phase 2.
  2. NEVER fabricate metric names or dimensions — verify against the service documentation or --help output.
  3. NEVER mix CLI commands between service versions — confirm which version/API you are targeting.
  4. ALWAYS use the discovery → verify → analyze chain — every resource referenced must have been discovered first.
  5. ALWAYS handle empty results gracefully — an empty response is valid data, not an error to retry.

Counter-Rationalizations

ShortcutCounterWhy
"I'll skip discovery and check known resources"Always run Phase 1 discovery firstResource names change, new resources appear — assumed names cause errors
"The user only asked for a quick check"Follow the full discovery → analysis flowQuick checks miss critical issues; structured analysis catches silent failures
"Default configuration is probably fine"Audit configuration explicitlyDefaults often leave logging, security, and optimization features disabled
"Metrics aren't needed for this"Always check relevant metrics when availableAPI/CLI responses show current state; metrics reveal trends and intermittent issues
"I don't have access to that"Try the command and report the actual errorAssumed permission failures prevent useful investigation; actual errors are informative

Common Pitfalls

  1. Database vs container throughput: Throughput can be provisioned at database level (shared) or container level (dedicated). Check both.
  2. Autoscale misread: autoscaleMaximumThroughput is the max -- actual consumption may be much lower. Always check NormalizedRUConsumption metric.
  3. Partition hot spots: High NormalizedRUConsumption on a single partition range indicates a hot partition. Check partition key design.
  4. Consistency tradeoffs: Strong consistency doubles RU cost for reads in multi-region setups. Session is the default and usually sufficient.
  5. Serverless vs provisioned: Serverless accounts do not have throughput settings. Check capacityMode in account properties.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Cloudflare GraphQL Analytics for zone traffic, firewall events, Workers metrics, and schema exploration. Use when querying Cloudflare analytics data or exploring the GraphQL API.

中文简介正在准备中。当前显示原文说明。

cloudthinker-ai/CloudSkills62026年4月5日 更新

Use when working with Alloydb — google AlloyDB instance analysis, query insights, columnar engine optimization, maintenance windows, and cluster health.

中文简介正在准备中。当前显示原文说明。

cloudthinker-ai/CloudSkills62026年4月5日 更新

Use when working with Aqua — aqua Security platform analysis. Covers container runtime protection, image assurance policies, compliance frameworks, vulnerability management, workload protection, and registry scanning. Use when analyzing container security posture, reviewing image compliance, investigating runtime alerts, or auditing security policies.

中文简介正在准备中。当前显示原文说明。

cloudthinker-ai/CloudSkills62026年4月5日 更新

Use when working with Bigquery — google BigQuery job analysis, slot utilization, cost analysis, dataset management, and query optimization.

中文简介正在准备中。当前显示原文说明。

cloudthinker-ai/CloudSkills62026年4月5日 更新

Use when working with Cassandra — apache Cassandra keyspace analysis, compaction strategies, repair status, nodetool operations, and cluster health monitoring.

中文简介正在准备中。当前显示原文说明。

cloudthinker-ai/CloudSkills62026年4月5日 更新

Use when working with Checkov — checkov infrastructure-as-code security scanning. Covers Terraform, CloudFormation, Kubernetes, and Dockerfile scanning, policy management, custom checks, compliance frameworks, and suppression management. Use when scanning IaC for security misconfigurations, evaluating compliance, managing custom policies, or reviewing scan results.

中文简介正在准备中。当前显示原文说明。

cloudthinker-ai/CloudSkills62026年4月5日 更新

查看 cloudthinker-ai 的全部技能

报告此技能的问题