Showing posts with label Monitoring. Show all posts
Showing posts with label Monitoring. Show all posts

Thursday, 14 May 2026

Introduction to Checkly



How Checkly works

Checkly is a SaaS synthetic monitoring platform — you define "checks" (HTTP requests or browser scripts), Checkly runs them on a schedule from probe locations around the world (or on-demand from CI), records latency/assertions/screenshots, and alerts you when they fail or get slow.
  
  Two main check types:

  - API checks — a single HTTP request with assertions on status, headers, body, response time.
  - Browser checks — a Playwright script run in a real headless Chromium against your deployed app.

There's also multi-step API checks (chain requests, e.g. login → use token → logout) and heartbeat checks (your job pings Checkly; alert if it stops).

Hearbeat vs Ping 

Heartbeats and pings are both vital network failure-detection mechanisms, but they differ in purpose: Heartbeats are proactive, periodic "I am alive" messages sent by an application to signal it is healthy, while Pings are reactive requests to check if a server is reachable. Heartbeats detect application crashes, while pings detect network downtime.

Checks are typically authored as code (Checkly CLI, TypeScript) and checkly deploy'd to the cloud. You can tag them (tags: ["auth"]), parametrise them with env vars like ENVIRONMENT_URL, and trigger them on-demand from CI — which is exactly what this PR does with npx checkly trigger --tags=auth.
  
  Runtime model:
  - Scheduled: every N minutes from chosen regions (e.g. us-east-2, eu-west-1) — catches regressions/outages between deploys.
  - Triggered from CI: post-deploy smoke test, results gate (or just annotate) the deploy.
  - Alerts: Slack/PagerDuty/email on failure, with retry/degraded thresholds to avoid flap.

  ---

  What it would check for this auth API
  
  Given the auth API's surface (login, OAuth, JWT issuance, admin endpoints), realistic auth-tagged checks:

  1. Health endpoint — basic liveness

  new ApiCheck("auth-health", {
    name: "Auth API – health",
    tags: ["auth"],
    frequency: 1, // minute
    locations: ["us-east-2", "eu-west-1"],
    request: {
      url: `${process.env.ENVIRONMENT_URL}/health`,
      method: "GET",
      assertions: [
        AssertionBuilder.statusCode().equals(200),
        AssertionBuilder.responseTime().lessThan(500),
        AssertionBuilder.jsonBody("$.status").equals("ok"),
      ],
    },
  });

  2. Login flow — happy path, returns a JWT

  new ApiCheck("auth-login", {
    name: "Auth API – login returns JWT",
    tags: ["auth"],
    request: {
      url: `${process.env.ENVIRONMENT_URL}/auth/login`,
      method: "POST",
      headers: [{ key: "Content-Type", value: "application/json" }],
      body: JSON.stringify({
        email: process.env.SYNTHETIC_USER_EMAIL,
        password: process.env.SYNTHETIC_USER_PASSWORD,
      }),
      assertions: [
        AssertionBuilder.statusCode().equals(200),
        AssertionBuilder.responseTime().lessThan(1500),
        AssertionBuilder.jsonBody("$.token").isNotNull(),
        // structural check on JWT shape
        AssertionBuilder.jsonBody("$.token").matches("^eyJ[A-Za-z0-9_-]+\\.[A-Za-z0-9_-]+\\.[A-Za-z0-9_-]+$"),
      ],
    },
  });
  
  3. Login — wrong password returns 401 (negative path)

  Catches the "accidentally accepts anything" class of regression.

  new ApiCheck("auth-login-bad-pw", {
    name: "Auth API – wrong password = 401",
    tags: ["auth"],
    request: {
      url: `${process.env.ENVIRONMENT_URL}/auth/login`,
      method: "POST",
      headers: [{ key: "Content-Type", value: "application/json" }],
      body: JSON.stringify({ email: process.env.SYNTHETIC_USER_EMAIL, password: "wrong" }),
      assertions: [AssertionBuilder.statusCode().equals(401)],
    },
  });
  
  4. Multi-step — login then call protected endpoint

  This is the most useful kind for an auth API, because it proves the token actually works.

  new MultiStepCheck("auth-token-roundtrip", {
    name: "Auth API – token works against /me",
    tags: ["auth"],
    code: { entrypoint: path.join(__dirname, "token-roundtrip.spec.ts") },
  });
  // token-roundtrip.spec.ts
  import { test, expect } from "@playwright/test";
  test("login then /me", async ({ request }) => {
    const login = await request.post(`${process.env.ENVIRONMENT_URL}/auth/login`, {
      data: { email: process.env.SYNTHETIC_USER_EMAIL, password: process.env.SYNTHETIC_USER_PASSWORD },
    });
    expect(login.ok()).toBeTruthy();
    const { token } = await login.json();
    
    const me = await request.get(`${process.env.ENVIRONMENT_URL}/me`, {
      headers: { Authorization: `Bearer ${token}` },
    });
    expect(me.status()).toBe(200);
    const body = await me.json();
    expect(body.email).toBe(process.env.SYNTHETIC_USER_EMAIL);
  });
  
  5. TLS & cert expiry

  A pure config check — useful because cert rotation is a classic outage cause.

  new ApiCheck("auth-tls", {
    name: "Auth API – TLS cert valid > 14d",
    tags: ["auth"],
    request: {
      url: `${process.env.ENVIRONMENT_URL}/health`,
      method: "GET",
      assertions: [AssertionBuilder.statusCode().equals(200)],
    },
    // Checkly surfaces cert expiry on the run; you set a threshold per check
  });
  
  6. Browser check — full login UX

    expect(login.ok()).toBeTruthy();
    const { token } = await login.json();

    const { token } = await login.json();

    const me = await request.get(`${process.env.ENVIRONMENT_URL}/me`, {
      headers: { Authorization: `Bearer ${token}` },
    });
    expect(me.status()).toBe(200);
    const body = await me.json();
    expect(body.email).toBe(process.env.SYNTHETIC_USER_EMAIL);
  });

  5. TLS & cert expiry

  A pure config check — useful because cert rotation is a classic outage cause.

  new ApiCheck("auth-tls", {
    name: "Auth API – TLS cert valid > 14d",
    tags: ["auth"],
    request: {
      url: `${process.env.ENVIRONMENT_URL}/health`,
      method: "GET",
      assertions: [AssertionBuilder.statusCode().equals(200)],
    },
    // Checkly surfaces cert expiry on the run; you set a threshold per check

  5. TLS & cert expiry

  A pure config check — useful because cert rotation is a classic outage cause.

  new ApiCheck("auth-tls", {
    name: "Auth API – TLS cert valid > 14d",
    tags: ["auth"],
    request: {
      url: `${process.env.ENVIRONMENT_URL}/health`,
      method: "GET",
      assertions: [AssertionBuilder.statusCode().equals(200)],
    },
    // Checkly surfaces cert expiry on the run; you set a threshold per check
  });

  new ApiCheck("auth-tls", {
    name: "Auth API – TLS cert valid > 14d",
    tags: ["auth"],
    request: {
      url: `${process.env.ENVIRONMENT_URL}/health`,
      method: "GET",
      assertions: [AssertionBuilder.statusCode().equals(200)],
    },
    // Checkly surfaces cert expiry on the run; you set a threshold per check
  });

  6. Browser check — full login UX

  Runs against the front-end but exercises the auth API end-to-end including redirects, cookies, CSRF.

  new BrowserCheck("auth-ui-login", {
    name: "Login UI works",
    tags: ["auth"],
    code: { entrypoint: path.join(__dirname, "login.spec.ts") },
  });
  import { test, expect } from "@playwright/test";
  test("user can sign in", async ({ page }) => {
    await page.goto(process.env.ENVIRONMENT_URL!);
    await page.getByLabel("Email").fill(process.env.SYNTHETIC_USER_EMAIL!);
    await page.getByLabel("Password").fill(process.env.SYNTHETIC_USER_PASSWORD!);
    await page.getByRole("button", { name: "Sign in" }).click();
    await expect(page.getByText("Dashboard")).toBeVisible({ timeout: 10_000 });
  });

  7. OAuth callback reachability

  Doesn't fully exercise the Google/Microsoft flow (those need real consent), but checks the callback
  endpoint responds correctly to a missing-code request — confirms route + handler are wired.

  new ApiCheck("auth-oauth-google-callback-shape", {
    name: "Auth API – Google OAuth callback exists",
    tags: ["auth"],
    request: {
      url: `${process.env.ENVIRONMENT_URL}/auth/google/callback`,
      method: "GET",
      assertions: [
        // 400 for missing `code`, not 404/500 — proves handler is mounted
        AssertionBuilder.statusCode().equals(400),
      ],
    },
  });

Thursday, 19 March 2026

Monitoring and Observability

 

Monitoring vs Observability

In the world of IT and DevOps, monitoring and observability are two related but distinct concepts used to manage system health and performance. 

Core Difference


The simplest way to distinguish them is:
  • Monitoring tells you what is happening (and when). It is reactive and focuses on known problems using predefined metrics.
  • Observability tells you why it is happening. It is proactive and uses the system's outputs to understand its internal state, especially for "unknown unknowns". 

Key Comparison Table


Feature         Monitoring                 Observability
----------           ---------------                    -----------------    
Purpose         Detect known issues Diagnose root causes
Perspective External (symptoms) Internal (system state)
Question         "Is the system healthy?" "Why is it behaving this way?"
Approach Reactive                         Proactive
Focus         "Known knowns"         "Unknown unknowns"
Data Types Metrics, logs                 Metrics, logs, and traces


Analogy: The Car

  • Monitoring is your dashboard. It has dials for speed and fuel, and a "check engine" light. It tells you if you are speeding or if something is broken.
  • Observability is the mechanic’s diagnostic tool. When the "check engine" light comes on, the mechanic plugs in a tool to see exactly which sensor failed and why, without having to take the entire engine apart. 

Common Tools

  • Monitoring Tools: Nagios, Zabbix, Prometheus.
  • Observability Platforms: Datadog, New Relic, Honeycomb, Dynatrace.


Three Pillars of Observability


The three pillars of observability—metrics, logs, and traces—are essential telemetry data types used to understand the internal state of complex, distributed systems. They enable teams to detect, investigate, and resolve performance issues by providing high-level trends, granular event details, and full request-flow paths. 

Metrics


Quantitative measurements over time (e.g., CPU usage, error rates).

Numerical measurements that describe the health, performance, and behavior of a system over time (e.g., CPU usage, error rates, throughput). They are ideal for alerting, capacity planning, and spotting trends or symptoms.


Logs


Granular, timestamped records of discrete events.

Timestamped, granular records of discrete events. They provide the detailed context (text or structured data) necessary to understand exactly what happened within an application or service.


Traces


End-to-end journeys of a single request through a distributed system, showing how different 

Records showing the journey of a single request as it travels through a distributed system, encompassing multiple services. They are critical for pinpointing bottlenecks, latency, or failures in microservices architectures. 

Why They Are Used Together


While metrics indicate that a problem exists, logs provide the context of why it happened, and traces show where it is occurring. Correlating these three data types provides actionable insights rather than just raw data.


Are Logs concern of Monitoring or Observabilty?

Both monitoring and observability deal with logs, but they do so in fundamentally different ways, representing a shift from simply knowing something is broken to understanding why. 

Monitoring is generally used to detect known issues using logs. It is reactive and focuses on pre-defined metrics or alert thresholds, such as alerting when error logs spike or when a specific error code appears.

Observability is used to investigate and understand the "why" behind issues by exploring logs, metrics, and traces together. It is proactive, allowing you to debug complex, distributed systems without needing to know every question ahead of time. 

Comparison: Logs in Monitoring vs. Observability


Feature                 Log Monitoring                                   Log Observability
----------                  ---------------------                                      -------------------------
Primary Question   What went wrong?                                   Why did it go wrong?
Approach        Reactive: Alerts when logs meet criteria   Proactive: Explores data to find root causes
Log Handling        Searchable, indexed logs for active alerts  Contextualized, correlated logs (with traces)
Data Usage       Simple monitoring and basic dashboards   Deep, ad-hoc, and exploratory analysis
Typical Usage       "Error rate > 5%"                                   "Why did this transaction fail?"


How They Work Together

Logs are one of the "three pillars" of observability—alongside metrics and traces—that provide the detailed, granular context necessary for troubleshooting, notes Grafana. 

Monitoring tells you the system is unhealthy (e.g., an alert fires because of high error rates in log files).
Observability allows you to use tools like Splunk or Datadog to dive into the logs and traces to find the specific line of code or database failure causing the issue

In short, monitoring is a component of observability—you cannot have true observability without comprehensive logging. 


Monitoring vs Observability on the example of AWS Lambda


For AWS Lambda, monitoring identifies what is wrong (e.g., an execution failed), while observability reveals why it happened by connecting logs, metrics, and traces across your entire serverless architecture. 

Monitoring AWS Lambda: Detecting the Known


Monitoring focuses on pre-defined health indicators. You use it to track "known-knowns" and trigger reactive alerts when thresholds are breached. 

  • Key Tool: Amazon CloudWatch collects standard metrics automatically.
  • Monitored Metrics:
    • Invocations: The total number of times your function runs.
    • Errors: The count of failed executions.
    • Duration: How long your function takes to run.
    • Throttles: Occurrences where invocations are blocked due to concurrency limits.
  • Example Scenario: You set a CloudWatch Alarm to notify you if your Lambda's error rate exceeds 5%. Monitoring tells you there is a problem, but not the specific line of code that caused it. 

Observability in AWS Lambda: Investigating the Unknown


Observability is a property of the system that allows you to understand its internal state from external outputs. It uses high-cardinality telemetry to investigate complex, distributed issues. 

  • Key Tool: AWS X-Ray provides distributed tracing to visualize the request path across multiple services.
  • Key Elements:
    • Distributed Traces: Seeing a "waterfall" view of a request as it moves from API Gateway to Lambda, then to DynamoDB.
    • Log Insights: Using CloudWatch Logs Insights to run ad-hoc queries across massive log volumes to find specific patterns.
    • Enhanced Instrumentation: Using libraries like AWS Lambda Powertools to add structured logging and context to your telemetry.
  • Example Scenario: You notice high latency in a specific user's request. Using X-Ray, you see that the Lambda function itself is fast, but it is waiting 2 seconds for a downstream third-party API call. Observability provided the "why". 

Comparison Summary for Lambda


Feature   Monitoring (CloudWatch)           Observability (X-Ray + Logs + Metrics)
----------     ---------------------------------            ---------------------------------------------------
Goal   Track health against thresholds.   Understand root causes and behavior.
Questions  "Is my function failing?"           "Why is this specific request slow?"
Visibility   Isolated metrics for one function.   Request paths across multiple services.
Action   Reactive (Alarms/Notifications).   Proactive (Debugging/Optimisation).


---

Thursday, 12 June 2025

Useful Kibana DevTools Queries






Elasticsearch’s query DSL is structured by query types.

Every top-level query must be one of the defined types:
  • match
  • term
  • range
  • bool
  • wildcard
  • query_string
  • function_score
  • etc.

Main APIs:

  • _cat - for a human-readable summary
  • _stats - for a detailed JSON response

Failed Queries


Example:

{
  "statusCode": 502,
  "error": "Bad Gateway",
  "message": "Client request timeout for: https://my.elastic-system.svc:9200 with request GET /my_index/_search?pretty=true"
}


A 502 Bad Gateway combined with a timeout usually means the Elasticsearch engine is struggling to process the request, or there is a networking bottleneck between Kibana and the database.



Cluster

To check cluster health:

GET /_cluster/health

GET /_cluster/health?level=shards

The output contains status which can be green, yellow or red.

To check status of each shard:

GET _cat/shards?v&h=index,shard,prirep,state,unassigned.reason,node

The output shows if shard is primary (p) or replica (r). It also shows the status which can be e.g. STARTED, UNASSIGNED  and reason which can be e.g. ALLOCATION_FAILED.

To sort the output by some column we can use s parameter:

GET /_cat/shards?v&h=index,shard,prirep,state,unassigned.reason,node,store&s=state
GET /_cat/shards?v&h=index,shard,prirep,state,unassigned.reason,node,store&s=node
GET /_cat/shards?v&h=index,shard,prirep,state,unassigned.reason,node,store&s=index 

To sort in descending order, append :desc to the name of the sorted column:

GET /_cat/shards?v&h=index,shard,prirep,state,unassigned.reason,node,store&s=store:desc



To get memory allocation and consumption per node:

GET /_cat/allocation?v&s=node

The output contains the following columns:
  • shards (number)
  • shards.undesired
  • write_load.forecast
  • disk.indices.forecast (in Gb or Tb)
  • disk.indices (in Gb or Tb)
  • disk.used (in Gb or Tb)
  • disk.avail (in Gb or Tb)
  • disk.total (in Gb or Tb)
  • disk.percent (number, %)
  • host (IP address)
  • ip (IP address)
  • node (node name or UNASSIGNED)
  • node.role (combination of cdfhilmrstw)

If some shard is not allocated, we can check the reason:

GET /_cluster/allocation/explain

To manually trigger retry of all previously failed shard allocations:

POST /_cluster/reroute?retry_failed=true

To check the progress, check the health of the cluster and:

GET /_cat/recovery/my_index?v



Index



In Elasticsearch, every index has a Mapping. Think of it like a database table definition. If a field isn't explicitly defined or hasn't been automatically detected from an uploaded document, Elasticsearch acts like that field doesn't exist. You can't sort by a column that the database doesn't know about!

To find out which fields exist in index:

GET /my_index/_mapping


To perform a search operation on a specific index:

GET /my_index/_search 

By itself (without a request body), it returns the first 10 documents by default. This request is the same as the above one:

GET /my_index/_search
{
  "query": {
    "match_all": {}
  }
}

In Kibana's Dev Tools, the query parameter in a GET request refers to the search query that defines which documents we want to retrieve from Elasticsearch. It's part of the request body and specifies the search criteria. The query parameter essentially tells Elasticsearch "find me documents that match these conditions." It's the core part of any search request and determines which documents from our index will be returned in the response.

The query object can contain various types of queries. Common query types:

match_all - Returns all documents:

{
  "query": {
    "match_all": {}
  }
}

match - Full-text search on a specific field:

{
  "query": {
    "match": {
      "field_name": "search_term"
    }
  }
}

term - Exact term matching:

{
  "query": {
    "term": {
      "status": "active"
    }
  }
}



To find all documents written in past 1 minute:

GET my_index/_search
{
  "query": {
    "range": {
        "timestamp": {
            "gte": "now-1m",
            "lte": "now"
        }
    }
  }
}


For X days use: Xd
For X hours use: Xh

bool


bool - Combine multiple queries with logical operators:

{
  "query": {
    "bool": {
      "must": [
        {"match": {"title": "elasticsearch"}},
        {"range": {"date": {"gte": "2023-01-01"}}}
      ]
    }
  }
}


bool is the workhorse of combinational querying in Elasticsearch.
It's like the logical brain of the query DSL.

A bool query lets you combine multiple conditions (AND, OR, NOT) into one query. Think of it like this:

(bool)
 ├── must → AND conditions
 ├── filter → AND but cheap
 ├── should → OR conditions
 └── must_not → NOT conditions


Without a bool query, Elasticsearch can only run one query condition at a time. But real searches need multiple conditions.

For example:
  • logs in the last 7 days
  • log_group is X
  • success = false
  • AND NOT status=200
  • AND (message contains A OR B)

You need logical operators → that's what bool provides.

Within bool we can use filter instead of must

filter is not a standalone query. It is only one part of the bool query type.

Filters are:
  • cached
  • more efficient
  • ideal for exact and boolean conditions

Everything inside filter is combined with AND.

"filter": [
  { cond1 },
  { cond2 },
  { cond3 }
]


This means:

cond1 AND cond2 AND cond3



filter is not a query type — it is an instruction telling the bool query how to treat subqueries (as cached, non-scoring, mandatory matches). 

So this:

"filter": [...]

doesn’t make sense by itself — filter what?

Whereas this:

"bool": {
  "filter": [...]
}

...means: "Run these filter queries together inside a boolean query."

Example:

GET logs-*/_search
{
  "query": {
    "bool": {
      "filter": [
        {
          "range": {
            "@timestamp": {
              "gte": "now-7d",
              "lte": "now"
            }
          }
        },
        {
          "term": {
            "log_group": "/aws/lambda/my-lambda"
          }
        },
        {
          "term": {
            "mycorp.message.my-lambda.success": false
          }
        }
      ]
    }
  }
}


All must clauses must also match.

"must": [
  { cond1 },
  { cond2 }
]

Meaning:

cond1 AND cond2


Inside should, clauses are OR unless minimum_should_match makes them mandatory.

"should": [
  { cond1 },
  { cond2 }
]

Meaning:

cond1 OR cond2

must_not: All must NOT match.

"must_not": [
  { cond1 }
]

Meaning:

NOT cond1



range - Query for values within a range:

{
  "query": {
    "range": {
      "age": {
        "gte": 18,
        "lte": 65
      }
    }
  }
}


To get the number of documents in an Elasticsearch index, you can use the _count API or the _stats API.

GET /my_index/_count

This will return a response like:

{
  "count": 12345,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped": 0,
    "failed": 0
  }
}


To get a certain number of documents, use size argument:

GET my_index/_search?size=900

We can also use _cat API:

GET /_cat/count/my_index?v

This will return output like:

epoch      timestamp count
1718012345 10:32:25  12345


GET /my_index/_stats

"indices": {
  "my_index": {
    "primaries": {
      "docs": {
        "count": 12345,
        "deleted": 12
      }
    }
  }
}


To get the union of all values of some field e.g. channel_type field across all documents in the my_index index, we can use an Elasticsearch terms aggregation:


GET my_index/_search
{
  "size": 0, 
  "aggs": {
    "unique_channel_types": {
      "terms": {
        "field": "channel_type.keyword",
        "size": 10000  // increase if you expect many unique values
      }
    }
  }
}


Explanation:
  • "size": 0: No documents returned, just aggregation results.
  • "terms": Collects unique values.
  • "channel_type.keyword": Use .keyword to aggregate on the raw value (not analyzed text).
  • "size": 10000: Max number of buckets (unique values) to return. Adjust as needed.

Response example:

{
  "aggregations": {
    "unique_channel_types": {
      "buckets": [
        { "key": "email", "doc_count": 456 },
        { "key": "push", "doc_count": 321 },
        { "key": "sms", "doc_count": 123 }
      ]
    }
  }
}

The "key" values in the buckets array are your union of channel_type values.


Let's assume that my_index has the timestamp field (as the root field...but it can be at any path in which case we'd need to adjust the query) is correctly mapped as a date type.


To get the oldest document:

GET my_index/_search
{
  "size": 1,
  "sort": [
    { "@timestamp": "asc" }
  ]
}


To get the newest document:

GET my_index/_search
{
  "size": 1,
  "sort": [
    { "@timestamp": "desc" }
  ]
}


Sorting by a field that isn't indexed or doc_values-enabled (especially on a large or unoptimized index) can cause the memory usage to spike and the request to hang until it times out.


How to get all possible values of some field in all documents added to index in last 24 hours?

We can use Terms Aggregation with Range Query:


GET /my_index/_search
{
  "size": 0,
  "query": {
    "range": {
      "@timestamp": {
        "gte": "now-24h/h",
        "lte": "now"
      }
    }
  },
  "aggs": {
    "unique_values": {
      "terms": {
        "field": "my_field.keyword",
        "size": 10000
      }
    }
  }
}


Check number of documents which are older than N days:


POST my_index/_count
{
  "query": {
    "range": {
      "@timestamp": {
        "lt": "now-Nd/d"
      }
    }
  }
}

Delete all documents older than N days:

POST my_index/_delete_by_query?conflicts=proceed&wait_for_completion=false
{
  "query": {
    "range": {
      "@timestamp": {
        "lt": "now-Nd/d"
      }
    }
  }
}

The output of the above command is task ID:

{
  "task": "BeJWGidDTtWkRL9aQEkjhg:26425516"
}

To check all tasks, grouped by node on which they are running:

GET _tasks

The output shows task's id, action name (e.g. "indices:data/write/bulk[s][p]", "cluster:monitor/tasks/lists[n]"), type (e.g. transport, monitoring, ...).

To check the task status:

GET _tasks/<task_id>

To check if any delete_by_query task is running and number of docs deleted so far:

GET _tasks?actions=*delete/byquery&detailed=true


Once delete_by_query task is completed: deletion is done, but disk space might not yet be reclaimed. To free disk space, run a forcemerge:

POST my_index/_forcemerge?only_expunge_deletes=true

For a huge shard, consider doing this after several weekly chunks, not after every single one, to reduce I/O spikes.

Check if any forcemerge tasks are running:

GET _tasks?actions=*forcemerge
GET _tasks?actions=*forcemerge&detailed=true
GET _tasks?actions=*forcemerge&detailed=true&group_by=parents

Check number of merges:

GET my_index/_stats?level=shards



To get number of shards in index:

GET my-index/_settings/?filter_path=**.number_of_shards


To get number of replicas:

GET my-index/_settings/?filter_path=**.number_of_replicas

Output:

{
  "my-index": {
    "settings": {
      "index": {
        "number_of_replicas": "1"
      }
    }
  }
}


How to find out disk size used by some index?

GET /_cat/indices/your-index-name*?v&h=index,docs.count,store.size,pri.store.size&s=store.size:desc
  • store.size: Total size on disk (includes all primary shards and replica shards).
  • pri.store.size: Size of only the primary shards (useful for knowing the "true" data size without redundancy).
  • s=store.size:desc: Sorts the list by size (useful if you are using a wildcard *).

If you need a precise number for an automated script or a deeper dive into memory usage, use the _stats endpoint.

GET /your-index-name*/_stats/store

In the JSON response, look for:
  • total.store.size_in_bytes: The exact byte count for the whole index (primaries + replicas).
  • primaries.store.size_in_bytes: The exact byte count for just the primary data.

To see which fields take up the most space:

POST /my-index/_disk_usage

To get the frequency of writes (document ingestion) over the last 24 hours, broken down by the hour, you should use a Date Histogram aggregation. In Elasticsearch, "writing into an index" typically equates to the creation of new documents with a @timestamp.

GET /your-index-name*/_search
{
  "size": 0, 
  "query": {
    "range": {
      "@timestamp": {
        "gte": "now-24h",
        "lte": "now"
      }
    }
  },
  "aggs": {
    "writes_per_hour": {
      "date_histogram": {
        "field": "@timestamp",
        "fixed_interval": "1h",
        "extended_bounds": {
          "min": "now-24h",
          "max": "now"
        }
      }
    }
  }
}


Breakdown of the Query:
  • "size": 0: Tells Elasticsearch we don't want to see the actual documents (the "hits"), just the statistical summary.
  • range: Filters the data to only include documents from the last 24 hours.
  • date_histogram: This is the magic part. It buckets your data by time.
  • fixed_interval: "1h": Groups the results into 1-hour chunks.
  • extended_bounds: Ensures that even if an hour had zero writes, it still shows up in your list as a "0" count rather than being skipped entirely.



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