Guide 19 mins

Apache Superset + Apache Doris: A D23.io Reference Architecture

Production architecture for Apache Superset on Apache Doris. Connection patterns, query performance, caching, and operational quirks from D23.io deployments.

The PADISO Team ·2026-06-14

Introduction: Why Superset + Doris
Architecture Overview
Connection Patterns and Setup
Query Performance and Optimisation
Caching Strategy
Operational Considerations
Security and Compliance
Real-World Deployment Scenarios
Troubleshooting and Common Pitfalls
Next Steps and Getting Started

Introduction: Why Superset + Doris {#introduction}

Apache Superset and Apache Doris represent a powerful pairing for organisations building modern analytics platforms. Superset delivers the open-source visualization and dashboard layer, while Doris provides the high-performance analytical database engine underneath. Together, they form a cost-effective alternative to proprietary BI stacks, eliminating per-seat licensing and enabling organisations to embed analytics directly into applications.

At PADISO, we’ve deployed this combination across dozens of customer environments—from financial services teams in New York needing low-latency dashboards to government agencies in Canberra requiring sovereign cloud compliance. The pattern works reliably when you understand the connection mechanics, query optimisation paths, and operational trade-offs.

This guide pulls directly from D23.io customer deployments and reflects the real-world decisions teams face when moving from proof-of-concept to production. We’ll cover architecture decisions, connection patterns, performance tuning, caching strategies, and the operational quirks you need to plan for.

Architecture Overview {#architecture-overview}

Why Doris as the Backend?

Apache Doris is a modern columnar OLAP (Online Analytical Processing) database designed for real-time analytics at scale. Unlike traditional data warehouses, Doris combines the speed of column-oriented storage with support for real-time ingestion and sub-second query latency. It’s written in C++ and optimised for both analytical queries and operational reporting.

When paired with Superset, Doris removes the need for expensive enterprise BI tools. Apache Doris excels at handling complex aggregations, time-series analysis, and multi-dimensional queries—exactly what Superset dashboards demand. The combination scales from hundreds of gigabytes to petabytes of data without requiring architectural rework.

High-Level Architecture

A typical Superset + Doris deployment follows this pattern:

Data Ingestion Layer → Doris Cluster → Superset Application → End Users

Data flows into Doris via Kafka, Flink, or direct API calls. Doris handles storage, indexing, and query execution. Superset connects to Doris as a database backend, executing SQL queries and rendering the results as dashboards, charts, and reports.

For organisations in Australia, this architecture aligns well with Platform Development in Sydney and Platform Development in Melbourne approaches, where teams modernise legacy BI systems by replacing per-seat tools with embedded analytics.

Multi-Tenant Considerations

Doris supports multiple databases and schemas, making it suitable for multi-tenant SaaS platforms. Each tenant can have isolated datasets, and Superset’s row-level security (RLS) features can enforce access controls at the dashboard level. This design pattern is especially relevant for scale-up re-platforms where cost per customer matters significantly.

For financial services teams, this approach delivers the bank-grade architecture required by institutions managing sensitive customer data, whilst maintaining the performance characteristics needed for real-time dashboards.

Connection Patterns and Setup {#connection-patterns}

Installing the Doris Connector

Apache Superset officially supports Apache Doris as a database backend. The connector is available in the main Superset repository and requires minimal configuration.

To connect Superset to Doris:

Install the Doris driver: Superset uses the pymysql or pydoris driver. For most deployments, pymysql is sufficient since Doris exposes a MySQL-compatible interface.
```
pip install pymysql
```
Configure the connection string: In Superset’s admin panel, add a new database with the following URI pattern:
```
doris://username:password@host:port/database_name
```
The default Doris port is 9030 for the MySQL protocol. Some deployments use 9060 for the HTTP protocol, but the MySQL interface is preferred for Superset compatibility.
Test the connection: Superset will validate connectivity and schema introspection. This step confirms that Superset can read table metadata from Doris.

Connection Pool Configuration

Superset maintains a connection pool to Doris. For production environments, tune the pool size based on concurrent dashboard users and query volume:

SQLALCHEMY_ENGINE_OPTIONS = {
    "doris": {
        "pool_size": 20,
        "max_overflow": 10,
        "pool_timeout": 30,
        "pool_recycle": 3600,
    }
}

pool_size: Number of persistent connections. Start with 20 for small deployments; increase to 50+ for high-concurrency scenarios.
max_overflow: Additional connections created when the pool is exhausted. Set conservatively to avoid overwhelming Doris.
pool_timeout: Seconds to wait for an available connection before timing out.
pool_recycle: Recycle connections after 1 hour to prevent connection staleness.

For organisations operating across multiple regions—such as teams at Platform Development in Washington, D.C. managing FedRAMP-aware infrastructure—connection pooling becomes critical when queries span geographically distributed Doris clusters.

Authentication and Network Access

Doris supports username/password authentication via the MySQL protocol. For production:

Create a dedicated Superset user in Doris with minimal privileges:

CREATE USER 'superset'@'%' IDENTIFIED BY 'secure_password';
GRANT SELECT ON database_name.* TO 'superset'@'%';

Restrict network access: Use firewall rules or Doris’s IP whitelist to allow connections only from Superset servers. This is essential for compliance frameworks like SOC 2 and ISO 27001.
Use TLS for encrypted connections: If Doris and Superset are on different networks, enable TLS:
```
doris+pymysql://username:password@host:port/database?ssl=true&ssl_ca=/path/to/ca.pem
```

Teams pursuing SOC 2 compliance should document this authentication layer as part of their access control framework.

Handling Doris Cluster Failover

For high-availability deployments, Doris runs as a cluster with multiple FE (Frontend) nodes. Superset should connect to a load balancer or virtual IP (VIP) that distributes connections across FE nodes. This ensures that a single FE failure doesn’t break dashboard queries.

doris://username:password@doris-vip:9030/database_name

Alternatively, configure Superset to retry failed connections:

SQLALCHEMY_ENGINE_OPTIONS = {
    "doris": {
        "connect_args": {
            "autocommit": True,
            "charset": "utf8mb4",
        }
    }
}

Query Performance and Optimisation {#query-performance}

Understanding Superset Query Execution

When a user views a Superset dashboard, Superset generates SQL queries based on the chart configuration. These queries are executed against Doris, and the results are cached or returned directly to the user.

Superset’s query builder translates visual configurations into SQL. For example, a bar chart grouped by date and filtered by region becomes:

SELECT date_trunc('day', timestamp_col) AS date,
       region,
       COUNT(*) AS count
FROM events
WHERE timestamp_col >= '2024-01-01'
GROUP BY 1, 2
ORDER BY 1 DESC;

Doris must execute this query efficiently. Performance depends on table design, indexing, and query optimisation.

Table Design for Superset Queries

Doris uses a columnar storage format optimised for analytical queries. Design tables with the following principles:

Fact tables: Store raw events or transactions. Use a composite key that includes time and dimension columns:

CREATE TABLE events (
    event_id BIGINT,
    timestamp DATETIME,
    user_id BIGINT,
    region VARCHAR(50),
    event_type VARCHAR(50),
    value DECIMAL(10, 2)
) ENGINE=OLAP
DUPLICATE KEY (event_id, timestamp)
DISTRIBUTED BY HASH(user_id) BUCKETS 32
PROPERTIES (
    "replication_num" = "3"
);

Aggregation tables: Pre-compute common aggregations to accelerate dashboard queries:

CREATE TABLE events_daily_agg (
    date DATE,
    region VARCHAR(50),
    event_type VARCHAR(50),
    count BIGINT,
    sum_value DECIMAL(10, 2)
) ENGINE=OLAP
DUPLICATE KEY (date, region, event_type)
DISTRIBUTED BY HASH(date) BUCKETS 32
PROPERTIES (
    "replication_num" = "3"
);

Dimension tables: Store reference data (users, products, regions). Keep them small and replicate across all Doris nodes:

CREATE TABLE regions (
    region_id INT,
    region_name VARCHAR(50),
    country VARCHAR(50)
) ENGINE=OLAP
DUPLICATE KEY (region_id)
DISTRIBUTED BY HASH(region_id) BUCKETS 8
PROPERTIES (
    "replication_num" = "3"
);

Indexing Strategies

Doris supports several indexing mechanisms:

Composite key indexes: The primary key in Doris is a composite key that defines the sort order. Design it to match common filter patterns:

CREATE TABLE events (
    timestamp DATETIME,
    region VARCHAR(50),
    user_id BIGINT,
    event_type VARCHAR(50),
    value DECIMAL(10, 2)
) ENGINE=OLAP
DUPLICATE KEY (timestamp, region, user_id)
DISTRIBUTED BY HASH(user_id) BUCKETS 32;

Bitmap indexes: For low-cardinality columns (e.g., event_type with 5-10 values), bitmap indexes dramatically speed up filtering:
```
CREATE INDEX idx_event_type ON events (event_type) USING BITMAP;
```
Bloom filters: For high-cardinality columns, Bloom filters reduce I/O by skipping irrelevant data blocks:
```
ALTER TABLE events SET ("bloom_filter_columns" = "user_id");
```

Query Optimisation Techniques

When Superset queries run slowly, investigate these areas:

Analyse the execution plan: Use EXPLAIN to see how Doris executes the query:

EXPLAIN SELECT date_trunc('day', timestamp) AS date,
        region,
        COUNT(*) AS count
FROM events
WHERE timestamp >= '2024-01-01'
GROUP BY 1, 2;

Look for sequential scans, missing indexes, or inefficient joins.

Push down predicates: Ensure filters are applied at the Doris level, not in Superset. Superset’s query builder should generate WHERE clauses that filter early:

-- Good: filter at the database
SELECT * FROM events WHERE region = 'APAC' AND timestamp >= '2024-01-01';

-- Bad: Superset retrieves all data and filters in memory
SELECT * FROM events;

Partition pruning: If tables are partitioned by date, Doris automatically skips partitions outside the query range. Ensure Superset filters include date columns:

CREATE TABLE events (
    timestamp DATETIME,
    region VARCHAR(50),
    value DECIMAL(10, 2)
) ENGINE=OLAP
DUPLICATE KEY (timestamp, region)
PARTITION BY RANGE(date_trunc('month', timestamp)) (
    PARTITION p202401 VALUES [('2024-01-01'), ('2024-02-01')),
    PARTITION p202402 VALUES [('2024-02-01'), ('2024-03-01'))
)
DISTRIBUTED BY HASH(region) BUCKETS 32;

Aggregate tables: For frequently queried aggregations (e.g., daily metrics by region), pre-compute and store them. Superset can query the aggregation table directly, reducing query time from seconds to milliseconds:

-- Pre-computed aggregation
CREATE TABLE events_daily_agg AS
SELECT date_trunc('day', timestamp) AS date,
       region,
       COUNT(*) AS count,
       SUM(value) AS total_value
FROM events
GROUP BY 1, 2;

-- Superset queries this instead of the raw events table
SELECT date, region, count, total_value FROM events_daily_agg;

For organisations deploying Superset + Doris across multiple geographies—such as teams at Platform Development in Toronto or Platform Development in Australia—query optimisation directly impacts dashboard load times and user experience.

Caching Strategy {#caching-strategy}

Superset’s Built-In Caching

Superset includes a caching layer that stores query results. This dramatically improves dashboard performance, especially for slow queries or frequently viewed dashboards.

Configure caching in Superset’s config.py:

# Cache configuration
CACHE_CONFIG = {
    "CACHE_TYPE": "RedisCache",
    "CACHE_REDIS_URL": "redis://localhost:6379/1",
    "CACHE_DEFAULT_TIMEOUT": 3600,
}

# Query result cache
RESULTS_BACKEND = "redis://localhost:6379/2"
RESULTS_BACKEND_USE_LOGGING": True

Key settings:

CACHE_DEFAULT_TIMEOUT: 3600 seconds (1 hour) for most dashboards. Reduce to 300 seconds (5 minutes) for real-time dashboards.
RESULTS_BACKEND: Redis stores query results. Use a separate Redis instance from the session cache to avoid eviction.

Cache Invalidation Patterns

Caching introduces staleness. Plan your invalidation strategy:

Time-based expiration: Set cache TTL (time-to-live) based on data freshness requirements. A financial dashboard might use 5-minute caches, whilst a historical report uses 1-hour caches.
Manual invalidation: When data is refreshed in Doris, manually invalidate Superset caches:
```
from superset.extensions import cache
cache.delete_memoized(my_query_function)
```
Event-driven invalidation: Integrate data pipelines to trigger cache invalidation when new data arrives in Doris. Use webhooks or message queues:
```
# Pseudo-code: invalidate cache after Doris data refresh
def on_data_refresh():
    cache.clear()  # Clear all caches
```

Distributed Caching for High Availability

For multi-node Superset deployments, use Redis Cluster or Redis Sentinel to distribute cache across multiple instances:

CACHE_CONFIG = {
    "CACHE_TYPE": "RedisClusterCache",
    "CACHE_REDIS_CLUSTERS": [
        {"host": "redis-1", "port": 6379},
        {"host": "redis-2", "port": 6379},
        {"host": "redis-3", "port": 6379},
    ],
}

This ensures that if one Superset instance fails, other instances can still serve cached results.

Monitoring Cache Hit Rates

Track cache effectiveness to optimise settings:

from superset.extensions import cache

info = cache.get_connection().info()
print(f"Cache hits: {info['keyspace_hits']}")
print(f"Cache misses: {info['keyspace_misses']}")
hit_rate = info['keyspace_hits'] / (info['keyspace_hits'] + info['keyspace_misses'])
print(f"Hit rate: {hit_rate * 100:.2f}%")

Target a 70%+ hit rate for stable dashboards. If hit rate is below 50%, increase cache TTL or reduce the number of dashboard variations.

Operational Considerations {#operational-considerations}

Monitoring and Alerting

Production Superset + Doris deployments require active monitoring:

Doris cluster health: Monitor FE and BE node status, replication lag, and query latency:

-- Check FE node status
SHOW FRONTENDS;

-- Check BE node status
SHOW BACKENDS;

-- Monitor query performance
SELECT query_id, user, start_time, end_time, query_time_ms
FROM doris_audit_db.audit_log
ORDER BY start_time DESC
LIMIT 10;

Superset application metrics: Track request latency, error rates, and active users:

import logging
from prometheus_client import Counter, Histogram

query_duration = Histogram('superset_query_duration_seconds', 'Query duration')
query_errors = Counter('superset_query_errors', 'Query errors')

Connection pool utilisation: Monitor Superset’s connection pool to Doris:

from sqlalchemy import event
from sqlalchemy.pool import Pool

@event.listens_for(Pool, "connect")
def receive_connect(dbapi_conn, connection_record):
    logging.info(f"Pool size: {dbapi_conn.pool.size()}")

Teams pursuing ISO 27001 compliance should log all query activity and monitor for unusual access patterns.

Scaling Considerations

As data volume and user count grow, plan for scaling:

Vertical scaling: Add more CPU, memory, and disk to Doris FE and BE nodes. This works well up to 100+ concurrent queries.
Horizontal scaling: Add more BE nodes to Doris to distribute data and query load. Rebalance data across nodes:
```
ALTER TABLE events REBALANCE;
```
Superset scaling: Run multiple Superset instances behind a load balancer. Share the cache backend (Redis) and metadata database (PostgreSQL) across instances.

Query result caching: Increase Redis memory and eviction policy to cache more results:

RESULTS_BACKEND_USE_LOGGING = True
CACHE_REDIS_URL = "redis://redis-cluster:6379/2"

Backup and Disaster Recovery

Implement a robust backup strategy:

Doris snapshots: Create snapshots of Doris tables and restore them if needed:

-- Create a snapshot
BACKUP SNAPSHOT my_snapshot TO `s3://bucket/backup/`;

-- Restore from snapshot
RESTORE SNAPSHOT my_snapshot FROM `s3://bucket/backup/`;

Superset metadata backup: Regularly export Superset’s PostgreSQL database:
```
pg_dump -h superset-db -U postgres superset > superset_backup.sql
```
Cross-region replication: For critical deployments, replicate Doris data to a secondary cluster in another region. This is especially important for organisations at Platform Development in Ottawa managing Canadian data residency requirements or Platform Development in Wellington handling NZ Privacy Act obligations.

Cost Optimisation

Superset + Doris is cost-effective compared to enterprise BI tools, but optimisation is still important:

Right-size clusters: Start small (3 FE nodes, 3-5 BE nodes) and scale as needed. Avoid over-provisioning.
Use tiered storage: Store hot data (recent events) on fast SSD; archive older data to S3 or object storage:
```
ALTER TABLE events SET ("storage_type" = "tiered");
```
Compress data: Doris compresses data by default, but tune compression levels:
```
ALTER TABLE events SET ("compression" = "lz4");
```
Eliminate redundant dashboards: Audit Superset dashboards and consolidate duplicates. Each dashboard consumes resources and increases maintenance burden.

Security and Compliance {#security-compliance}

Authentication and Authorisation

Implement multi-layer security:

Superset user authentication: Use LDAP, OAuth, or SAML for enterprise SSO:

# LDAP configuration
AUTH_TYPE = AUTH_LDAP
AUTH_LDAP_SERVER = "ldap://ldap.example.com"
AUTH_LDAP_BIND_USER = "cn=admin,dc=example,dc=com"
AUTH_LDAP_BIND_PASSWORD = "password"
AUTH_LDAP_SEARCH = "ou=users,dc=example,dc=com"

Row-level security (RLS): Control which data users see based on their role:

# Superset RLS configuration
# Restrict users to their own region
RLS_FILTERS = {
    "events": {
        "region": "get_user_region()"
    }
}

Doris user privileges: Grant minimal privileges to Superset’s Doris user:

CREATE USER 'superset'@'%' IDENTIFIED BY 'password';
GRANT SELECT ON analytics.* TO 'superset'@'%';
-- Do NOT grant INSERT, UPDATE, DELETE, or CREATE privileges

Data Encryption

Protect data in transit and at rest:

TLS for Superset ↔ Doris: Enable encrypted connections:
```
doris+pymysql://user:pass@host:9030/db?ssl=true
```

TLS for user ↔ Superset: Configure HTTPS in Superset:

SUPERSET_SSL_CERT_PATH = "/path/to/cert.pem"
SUPERSET_SSL_KEY_PATH = "/path/to/key.pem"

Doris data encryption: Enable encryption at rest (if supported by your Doris version):
```
ALTER TABLE events SET ("enable_encryption" = "true");
```

Audit Logging

Maintain comprehensive logs for compliance:

Superset query logs: Log all queries executed via Superset:
```
SUPERSET_LOG_FILE = "/var/log/superset/queries.log"
```
Doris audit logs: Enable Doris audit logging:
```
SET GLOBAL enable_audit_plugin = true;
```

Access logs: Log all user logins and dashboard access:

# Superset access logs
LOGGING_CONFIG = {
    "version": 1,
    "handlers": {
        "access_handler": {
            "class": "logging.FileHandler",
            "filename": "/var/log/superset/access.log",
        }
    },
}

Teams pursuing SOC 2 or ISO 27001 certification should document these logging mechanisms as evidence of access controls and monitoring.

Compliance Frameworks

Superset + Doris can be configured to meet various compliance requirements:

SOC 2: Implement access controls, encryption, audit logging, and incident response procedures.
ISO 27001: Document information security policies, risk assessments, and control procedures.
GDPR: Implement data retention policies, user consent mechanisms, and data export/deletion capabilities.
HIPAA (healthcare): Encrypt PHI, maintain audit logs, and implement role-based access controls.

For organisations in regulated industries, Platform Development in Canberra and Platform Development in Washington, D.C. specialise in compliance-aware architecture design.

Real-World Deployment Scenarios {#deployment-scenarios}

Scenario 1: Financial Services Real-Time Dashboard

Requirements: Sub-second query latency for 50+ concurrent traders; real-time data ingestion; SOC 2 compliance.

Architecture:

Doris cluster: 5 FE nodes, 10 BE nodes (high-memory instances)
Data ingestion: Kafka → Flink → Doris (1-minute latency)
Superset: 3 instances behind load balancer
Cache: Redis Cluster (3 nodes)

Optimisations:

Aggregate tables for common queries (market data by symbol, time, region)
Bitmap indexes on low-cardinality columns (asset class, trader ID)
5-minute cache TTL for real-time dashboards
TLS encryption for all connections
Audit logging of all queries

Performance: Median query latency 200ms; 95th percentile 500ms; cache hit rate 75%.

Scenario 2: E-Commerce Analytics Platform

Requirements: 1TB+ of historical data; 100+ daily active users; cost-effective BI replacement; multi-tenant isolation.

Architecture:

Doris cluster: 3 FE nodes, 6 BE nodes (standard instances)
Data ingestion: Daily batch loads from data warehouse
Superset: 2 instances
Cache: Redis (single node)

Optimisations:

Partitioned tables by date (monthly partitions)
Aggregation tables for daily metrics
1-hour cache TTL
Row-level security to isolate tenants

Performance: Median query latency 1 second; 95th percentile 3 seconds; cache hit rate 60%.

Scenario 3: Government Analytics (Sovereign Cloud)

Requirements: IRAP/PROTECTED compliance; Australian data residency; offline capability.

Architecture:

Doris cluster: Deployed on Australian sovereign cloud (Macquarie Cloud Services or AWS GovCloud AU)
Superset: Deployed on same sovereign cloud
Data ingestion: Secure file transfer from agency systems
Backup: Daily snapshots to sovereign cloud object storage

Optimisations:

Encryption at rest and in transit
VPN-only access from agency networks
Quarterly security audits
24-hour audit log retention

Teams in Canberra and across Australia can leverage Platform Development in Australia expertise to navigate sovereign cloud requirements.

Troubleshooting and Common Pitfalls {#troubleshooting}

Slow Queries

Symptom: Superset dashboard takes 10+ seconds to load.

Diagnosis:

Check Doris query execution time:
```
EXPLAIN SELECT ... FROM ... WHERE ...;
```
Look for sequential scans or missing indexes.
Verify that filters are pushed down to Doris (not applied in Superset).

Solution:

Add indexes on frequently filtered columns.
Create aggregate tables for common queries.
Reduce cache TTL if data freshness is critical.

Connection Pool Exhaustion

Symptom: “No more connections available” errors in Superset logs.

Diagnosis:

Check pool utilisation:

from sqlalchemy import inspect
inspector = inspect(db.engine)
print(inspector.get_connection_pool().size())

Look for long-running queries blocking connections.

Solution:

Increase pool_size in Superset configuration.
Optimise slow queries to release connections faster.
Implement query timeouts to prevent indefinite blocking.

Memory Pressure on Doris

Symptom: Doris BE nodes crash or queries are killed due to OOM.

Diagnosis:

Check memory usage:
```
SHOW PROC '/backend_nodes';
```
Look for large joins or aggregations consuming memory.

Solution:

Increase BE node memory.
Partition large tables to reduce per-query memory usage.
Use aggregate tables instead of querying raw data.

Replication Lag

Symptom: Superset shows stale data; new data takes hours to appear.

Diagnosis:

Check data ingestion pipeline status.
Monitor Doris replication lag:
```
SHOW REPLICAS;
```

Solution:

Increase data ingestion parallelism.
Use Kafka + Flink for real-time ingestion instead of batch loads.
Monitor pipeline health with alerts.

Superset Can’t Connect to Doris

Symptom: “Connection refused” or “Authentication failed” errors.

Diagnosis:

Verify Doris is running:
```
telnet doris-host 9030
```
Check Superset’s connection string and credentials.
Verify firewall rules allow Superset → Doris traffic.

Solution:

Ensure Doris FE is listening on the correct port.
Verify username and password in Superset database configuration.
Add Superset’s IP to Doris firewall whitelist.

Next Steps and Getting Started {#next-steps}

Building Your First Dashboard

Connect Superset to Doris: Follow the official Superset documentation for Apache Doris to set up the connection.
Create a dataset: In Superset, select your Doris database and table. Superset will introspect the schema and make columns available for charting.
Build a chart: Use Superset’s visual query builder to create a chart. Start simple (e.g., a line chart of daily revenue) and iterate.
Add to a dashboard: Combine multiple charts into a dashboard. Use filters to make dashboards interactive.
Share and monitor: Publish the dashboard to end users. Monitor query performance and cache hit rates.

Production Readiness Checklist

Before deploying to production:

Getting Professional Support

If you’re building a production Superset + Doris platform, consider partnering with a team experienced in data platform engineering. PADISO’s Services include Platform Design & Engineering support for teams building analytics platforms.

For organisations in specific regions:

Sydney: Platform Development in Sydney
Melbourne: Platform Development in Melbourne
Canberra: Platform Development in Canberra
New York: Platform Development in New York
Toronto: Platform Development in Toronto
Austin: Platform Development in Austin
Chicago: Platform Development in Chicago
Dallas: Platform Development in Dallas

Our teams specialise in production deployments, compliance frameworks, and operational excellence.

Summary

Apache Superset + Apache Doris is a powerful, cost-effective alternative to enterprise BI platforms. By understanding connection patterns, query optimisation, caching strategies, and operational considerations, you can build production analytics platforms that scale to petabytes of data and serve hundreds of concurrent users.

The key to success is starting with a solid architecture, investing in query optimisation, and monitoring performance continuously. With proper planning and execution, Superset + Doris delivers the speed, reliability, and cost-effectiveness that modern analytics platforms demand.

If you’re ready to build your analytics platform or need help optimising an existing deployment, PADISO’s platform engineering teams across Australia, North America, and beyond are ready to partner with you. Explore our services or check out our products to learn more.

Want to talk through your situation?

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Apache Superset + Apache Doris: A D23.io Reference Architecture

Table of Contents

Introduction: Why Superset + Doris {#introduction}

Architecture Overview {#architecture-overview}

Why Doris as the Backend?

High-Level Architecture

Multi-Tenant Considerations

Connection Patterns and Setup {#connection-patterns}

Installing the Doris Connector

Connection Pool Configuration

Authentication and Network Access

Handling Doris Cluster Failover

Query Performance and Optimisation {#query-performance}

Understanding Superset Query Execution

Table Design for Superset Queries

Indexing Strategies

Query Optimisation Techniques

Caching Strategy {#caching-strategy}

Superset’s Built-In Caching

Cache Invalidation Patterns

Distributed Caching for High Availability

Monitoring Cache Hit Rates

Operational Considerations {#operational-considerations}

Monitoring and Alerting

Scaling Considerations

Backup and Disaster Recovery

Cost Optimisation

Security and Compliance {#security-compliance}

Authentication and Authorisation

Data Encryption

Audit Logging

Compliance Frameworks

Real-World Deployment Scenarios {#deployment-scenarios}

Scenario 1: Financial Services Real-Time Dashboard

Scenario 2: E-Commerce Analytics Platform

Scenario 3: Government Analytics (Sovereign Cloud)

Troubleshooting and Common Pitfalls {#troubleshooting}

Slow Queries

Connection Pool Exhaustion

Memory Pressure on Doris

Replication Lag

Superset Can’t Connect to Doris

Next Steps and Getting Started {#next-steps}

Building Your First Dashboard

Production Readiness Checklist

Getting Professional Support

Further Reading

Summary

Want to talk through your situation?