---
title: "Features"
description: "Explore Qdrant features: dashboard, monitoring, scaling, snapshots, and integrations"
---

import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import { QdrantFeaturesNav } from './QdrantFeaturesCards'

# Features

<QdrantFeaturesNav />

---

## 1. Qdrant Dashboard

Qdrant provides a built-in web dashboard for managing collections, running queries, and exploring data.

### How to Access

1.  Navigate to your database in the TIR portal.
2.  Click **Connect** to open the Qdrant Dashboard.
3.  Enter your **API Key** and click **Apply**.

### Dashboard Sections

| Section | Purpose |
|---------|---------|
| **Collections** | Create, view, and delete collections. Browse points, view collection info, and visualize vectors. |
| **Console** | Execute Qdrant API commands interactively. Browse available API commands and run them directly. |
| **Tutorial** | Interactive API tutorial provided by Qdrant to learn how the APIs work. |
| **Datasets** | Sample data for testing and experimentation. |

:::tip
Use the Console section to test API calls before integrating them into your application.
:::

---

## 2. Monitoring

Track your cluster's health and performance through the **Monitoring** tab on the database details page.

### Hardware Metrics

| Metric | Description |
|--------|-------------|
| **CPU Usage** | Processor utilization percentage per node |
| **Memory Usage** | RAM consumption per node |

### Service Metrics

| Metric | Description |
|--------|-------------|
| **REST Request Count** | Total HTTP API requests (with failure breakdown) |
| **gRPC Request Count** | Total gRPC API requests (with failure breakdown) |
| **REST Response Duration** | Average latency of HTTP API responses per cluster peer |
| **gRPC Response Duration** | Average latency of gRPC API responses per cluster peer |
| **Cluster Pending Operations** | Operations waiting in the cluster queue |

### Cluster Gauges

| Gauge | Description |
|-------|-------------|
| **Total Collections** | Number of collections in the cluster |
| **Total Vectors** | Total vector count across all collections |
| **Cluster Peers** | Number of active nodes in the cluster |

**Available time intervals:** 5 minutes, 1 hour, 24 hours, 7 days, 30 days.

---

## 3. Scaling

TIR deploys Qdrant in distributed mode where multiple nodes form a cluster.

### Upscaling Nodes

1.  Click the **Actions** icon for your database.
2.  Select **Scale Up**.
3.  Choose the new node count and click **Update**.

:::info Important
*   You can scale from **3 to 10 nodes**. Downscaling is not supported.
*   **New nodes start empty.** Data is not automatically rebalanced.
*   See [Making Use of New Nodes](#making-use-of-new-nodes) below to distribute data to new nodes.
:::

### Resizing Disk

1.  Navigate to your database and open the **Resize Disk** tab.
2.  Select the new storage size and confirm.

:::warning
Disk size can be increased from 10 GB to 1,000 GB but **cannot be reduced** once increased. Additional storage is charged per node.
:::

### Making Use of New Nodes

When you add nodes, they start empty. You have three options to use them:

| Option | When to Use |
|--------|-------------|
| **Create a new collection** | New data automatically distributes across all nodes based on shard count and replication factor. |
| **Replicate existing shards** | Copy shard data to the new node for redundancy. |
| **Move existing shards** | Relocate shards without duplicating data. |

#### Creating a New Collection

```bash
curl -X PUT \
  -H "api-key: <your-api-key>" \
  -d '{
    "vectors": { "size": 300, "distance": "Cosine" },
    "shard_number": 8,
    "replication_factor": 2
  }' \
  https://<your-endpoint-url>:6333/collections/<collection_name>
```

:::tip
Set `shard_number` as a multiple of your node count for even distribution.
:::

#### Replicating Existing Data

First, get cluster info to find peer IDs and shard assignments:

```bash
curl -X GET \
  -H "api-key: <your-api-key>" \
  https://<your-endpoint-url>:6333/collections/<collection_name>/cluster
```

Replicate a shard to a new node:

```bash
curl -X PUT \
  -H "api-key: <your-api-key>" \
  -d '{
    "replicate_shard": {
      "shard_id": 0,
      "from_peer_id": 123,
      "to_peer_id": 456
    }
  }' \
  https://<your-endpoint-url>:6333/collections/<collection_name>/cluster
```

Drop a replica from a node:

```bash
curl -X POST \
  -H "api-key: <your-api-key>" \
  -d '{
    "drop_replica": {
      "shard_id": 0,
      "peer_id": 123
    }
  }' \
  https://<your-endpoint-url>:6333/collections/<collection_name>/cluster
```

#### Moving Shards

Move a shard between nodes without duplicating data:

```bash
curl -X POST \
  -H "api-key: <your-api-key>" \
  -d '{
    "move_shard": {
      "shard_id": 0,
      "from_peer_id": 123,
      "to_peer_id": 456
    }
  }' \
  https://<your-endpoint-url>:6333/collections/<collection_name>/cluster
```

**Transfer methods:**

| Method | Description |
|--------|-------------|
| **stream_records** (default) | Streams records to the target node in batches. |
| **snapshot** | Transfers the shard including index and quantized data via a snapshot. |

To use snapshot transfer, add `"method": "snapshot"` to the `move_shard` payload.

---

## 4. Snapshots

### Managed Snapshots (TIR)

TIR provides one-click cluster-wide snapshots that back up all data across all nodes.

#### Create a Snapshot

1.  Click the **Take Snapshot** icon on the Vector Database list or from the **Actions** menu.
2.  Monitor the status in the **Snapshots** tab.

:::info
You cannot create another snapshot or scale your cluster while a snapshot is in progress.
:::

#### Restore a Snapshot

1.  Open the **Snapshots** tab for your database.
2.  Click the **Restore** icon next to the snapshot.
3.  Monitor the restoration status.

:::warning
*   The database must be in **Running** state to restore.
*   During restoration, the dashboard and upscaling operations are disabled.
*   Do not perform write operations during restoration.
:::

#### Delete a Snapshot

1.  Open the **Snapshots** tab.
2.  Click the **Delete** icon next to the snapshot.

:::info
You cannot delete a snapshot while its restoration is in progress.
:::

### API Snapshots (Per-Node, Per-Collection)

Qdrant also provides per-node, per-collection snapshot APIs. These snapshots are stored on the node's local disk.

:::info
You must create and restore snapshots for each node per collection separately using the node-specific URI: `https://<endpoint>:6333/node-{node}` where node ranges from `0` to `replicas - 1`.
:::

#### Create a Snapshot

<Tabs>
  <TabItem value="Python" label="Python" default>

```python
from qdrant_client import QdrantClient

client = QdrantClient(
    host="<endpoint-url>", port=6333,
    prefix="node-<node>", api_key="<api-key>"
)
client.create_snapshot(collection_name="<collection_name>")
```

  </TabItem>
  <TabItem value="cURL" label="cURL">

```bash
curl -X POST \
  -H "api-key: <your-api-key>" \
  https://<endpoint-url>:6333/node-<node>/collections/<collection_name>/snapshots
```

  </TabItem>
</Tabs>

#### List Snapshots

<Tabs>
  <TabItem value="Python" label="Python" default>

```python
client.list_snapshots(collection_name="<collection_name>")
```

  </TabItem>
  <TabItem value="cURL" label="cURL">

```bash
curl -X GET \
  -H "api-key: <your-api-key>" \
  https://<endpoint-url>:6333/node-<node>/collections/<collection_name>/snapshots
```

  </TabItem>
</Tabs>

#### Download a Snapshot File

```bash
curl -X GET \
  -H "api-key: <your-api-key>" \
  https://<endpoint-url>:6333/node-<node>/collections/<collection_name>/snapshots/<snapshot_name>
```

#### Restore a Snapshot

<Tabs>
  <TabItem value="Python" label="Python" default>

```python
client.recover_snapshot(
    "<collection_name>",
    "<file_location>"
)
```

  </TabItem>
  <TabItem value="cURL" label="cURL">

```bash
curl -X PUT \
  -H "api-key: <your-api-key>" \
  -d '{ "location": "<file_location>" }' \
  https://<endpoint-url>:6333/node-<node>/collections/<collection_name>/snapshots/recover
```

  </TabItem>
</Tabs>

You can also restore from an uploaded file:

```bash
curl -X POST \
  'https://<endpoint-url>:6333/node-<node>/collections/<collection_name>/snapshots/upload?priority=snapshot' \
  -H 'api-key: <your-api-key>' \
  -H 'Content-Type: multipart/form-data' \
  -F 'snapshot=@/path/to/<file_name>.snapshot'
```

#### Snapshot Recovery Priorities

| Priority | Behavior |
|----------|----------|
| **replica** (default) | Prefer existing data over snapshot data. |
| **snapshot** | Prefer snapshot data over existing data. |
| **no_sync** | Restore without any additional synchronization. |

#### Delete a Snapshot

<Tabs>
  <TabItem value="Python" label="Python" default>

```python
client.delete_snapshot(
    collection_name="<collection_name>",
    snapshot_name="<snapshot_name>"
)
```

  </TabItem>
  <TabItem value="cURL" label="cURL">

```bash
curl -X DELETE \
  -H "api-key: <your-api-key>" \
  https://<endpoint-url>:6333/node-<node>/collections/<collection_name>/snapshots/<snapshot_name>
```

  </TabItem>
</Tabs>

---

## 5. Access Control

Each Qdrant database has two access keys:

| Key Type | Purpose |
|----------|---------|
| **API Key** | Full read/write access to all collections and operations. |
| **Read-Only API Key** | Read-only access for queries and listing operations. |

:::tip
Use the read-only key for client applications that only need to query data.
:::

---

## 6. Integrations

### LangChain

[LangChain](https://www.langchain.com/) simplifies building LLM-powered applications. Integrate Qdrant as a vector store for search and retrieval.

**Install:**

```bash
pip install langchain langchain-community qdrant-client
```

**Connect, embed, and search:**

```python
from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import CharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Qdrant

# Load and chunk documents
loader = TextLoader("<your-file>")
documents = loader.load()
docs = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0).split_documents(documents)

# Embed and insert into Qdrant
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
qdrant = Qdrant.from_documents(
    docs, embeddings,
    host="<your-endpoint-url>", port=6333,
    api_key="<your-api-key>",
    collection_name="my_documents",
)

# Similarity search
found_docs = qdrant.similarity_search_with_score("<your-query>")
document, score = found_docs[0]

# MMR search (diverse results)
found_docs = qdrant.max_marginal_relevance_search("<your-query>", k=2, fetch_k=10)
```

For more, see the [Qdrant LangChain Documentation](https://qdrant.tech/documentation/frameworks/langchain/).

### LlamaIndex

[LlamaIndex](https://www.llamaindex.ai/) connects your private data with LLMs. Use Qdrant as a vector store for indexing and retrieval.

**Install:**

```bash
pip install llama-index llama-index-vector-stores-qdrant qdrant-client
```

**Connect, embed, and search:**

```python
import qdrant_client
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext, Settings
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.embeddings.fastembed import FastEmbedEmbedding

# Configure embedding model
Settings.embed_model = FastEmbedEmbedding(model_name="BAAI/bge-base-en-v1.5")
Settings.llm = None

# Load documents
documents = SimpleDirectoryReader("<your-data-dir>").load_data()

# Connect and insert into Qdrant
client = qdrant_client.QdrantClient(
    host="<your-endpoint-url>", port=6333, api_key="<your-api-key>"
)
vector_store = QdrantVectorStore(client=client, collection_name="<collection_name>")
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)

# Query
query_engine = index.as_query_engine()
response = query_engine.query("<your-query>")
```

For more, see the [Qdrant LlamaIndex Documentation](https://qdrant.tech/documentation/frameworks/llamaindex/).



---