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Example 08: Server Mode, Studio & Concurrent HTTP Clients

This example demonstrates how to run ArcadeDB in Server Mode, which enables the HTTP REST API and the Studio Web UI while maintaining embedded Python access. It also showcases concurrent load testing and polyglot querying (SQL + OpenCypher).

View source code

Overview

Embedded vs Server

The arcadedb-embedded Python library is primarily designed for embedded usage (running the database directly within your Python process for maximum performance). However, as shown in this example, it can also launch the full ArcadeDB server to support external clients (via HTTP/REST) or the Studio UI, effectively acting as a database server managed by Python.

ArcadeDB can run in two modes:

  1. Embedded (Default): Direct JVM access, no network overhead, single process.
  2. Server Mode: Starts an HTTP server, enabling remote access and Studio UI.

This example shows how to start the server programmatically, simulate concurrent clients, and execute mixed workloads.

Key Concepts

1. Starting the Server

You can start the server using arcadedb.create_server(). This starts the JVM and the embedded Jetty server.

import arcadedb_embedded as arcadedb

# Configure and start server
server = arcadedb.create_server(
    root_path="./my_test_databases",
    root_password="playwithdata",  # Required for root access
    config={
        "http_port": 2480,
        "host": "0.0.0.0"
    }
)

server.start()

2. Accessing Studio

Once started, the ArcadeDB Studio is available at:

http://localhost:2480

You can log in with:

  • User: root
  • Password: playwithdata
  • Database: stackoverflow_small_db_graph

3. Concurrent Client Simulation

The example uses Python's concurrent.futures.ThreadPoolExecutor to simulate multiple clients hitting the server simultaneously via the HTTP API.

def demonstrate_concurrency(db_name, num_clients=6):
    with concurrent.futures.ThreadPoolExecutor(max_workers=num_clients) as executor:
        futures = []
        for i, query_def in enumerate(WORKLOAD_QUERIES):
            # Distribute queries among simulated clients
            client_id = (i % num_clients) + 1
            futures.append(executor.submit(run_client_query, client_id, db_name, query_def))

4. Polyglot Workload

The workload consists of a mix of SQL and OpenCypher queries to demonstrate the server's ability to handle different query languages concurrently.

SQL Example:

SELECT DisplayName, out('EARNED').size() as badge_count
FROM User
ORDER BY badge_count DESC LIMIT 1

OpenCypher Example:

MATCH (u:User)-[:ASKED]->(q:Question)-[:HAS_ANSWER]->(a:Answer)
WHERE (u)-[:ANSWERED]->(a)
RETURN count(DISTINCT u) as count

Running the Example

cd bindings/python/examples
python 08_server_mode_rest_api.py

When to Use Server Mode?

Use Case Embedded Mode Server Mode
Single Python Script ✅ Best ❌ Overkill
Data Analysis (Pandas) ✅ Best ❌ Slower
Web App Backend ✅ Good ✅ Good (if need external access)
Debugging / Visualization ❌ No UI ✅ Studio UI
Multi-Process Access ❌ Locked ✅ HTTP API
Microservices ❌ Hard ✅ HTTP API

Next Steps

This concludes the example series! You have learned:

  1. Basics: Documents, SQL, CRUD
  2. Graph: Vertices, Edges, Traversal
  3. Vector: Embeddings, Semantic Search
  4. Import: CSV, Batching, ETL
  5. Advanced: Multi-model, Hybrid Search
  6. Server: HTTP API, Studio, Concurrency