Presto/Trino

In the evolving landscape of big data analytics, organizations face the perpetual challenge of querying vast data lakes efficiently. Enter Presto (now also known as Trino) – a distributed SQL query engine that has fundamentally transformed how analysts and data engineers interact with massive datasets. This powerful open-source technology enables lightning-fast SQL queries across diverse data sources, breaking down data silos while maintaining exceptional performance.

Presto was born in 2012 at Facebook (now Meta) when engineers faced a critical challenge: how to perform interactive analytics on their enormous data lake stored in HDFS. Traditional tools like Hive, while powerful, were too slow for ad-hoc exploration and analysis. The engineering team, led by Martin Traverso, Dain Sundstrom, David Phillips, and Eric Hwang, set out to build a solution from scratch.

Their vision was ambitious but clear – create a distributed SQL engine that could:

• Query petabytes of data interactively
• Support standard ANSI SQL
• Connect to multiple data sources
• Scale horizontally with minimal overhead
• Maintain high performance without data movement

The result was Presto – a revolutionary query engine that separated compute from storage and delivered query results at unprecedented speeds. Facebook open-sourced the project in 2013, and it quickly gained adoption across the tech industry.

In 2019, a significant development occurred when the original Presto creators left Facebook and established the Presto Software Foundation, rebranding their version as Trino (formerly PrestoSQL) due to trademark issues. Today, both Presto (maintained by the Linux Foundation’s Presto Foundation) and Trino continue to evolve in parallel, sharing the same core architecture with some divergent features.

The secret to Presto’s performance lies in its distributed architecture, designed specifically for low-latency, in-memory processing of queries:

The coordinator serves as the brain of the operation:

• Receives SQL queries from clients
• Parses and analyzes the SQL
• Creates a distributed execution plan
• Assigns work to worker nodes
• Collects and returns results to the client

Workers form the processing backbone:

• Execute tasks assigned by the coordinator
• Process data in parallel across the cluster
• Exchange intermediate results with other workers
• Leverage pipelined execution for efficiency

Perhaps the most powerful feature of Presto/Trino is its connector architecture:

• Pluggable Interface: Connectors provide a standardized way to access different data sources
• Push-Down Optimization: Capable connectors offload filtering and other operations to the underlying system
• Schema Discovery: Automatically discovers available tables and their structures
• Native Access: Optimized for each data source’s unique characteristics

Popular connectors include:

• Hive: For data in HDFS/S3 (Parquet, ORC, etc.)
• Cassandra/ScyllaDB: For wide-column NoSQL data
• MySQL/PostgreSQL/SQL Server: For relational databases
• MongoDB: For document databases
• Kafka: For streaming data
• Elasticsearch: For search indexes
• Redis: For key-value data
• Google BigQuery: For Google Cloud data
• Snowflake: For cloud data warehouses

The remarkable aspect is that a single SQL query can seamlessly join data across these diverse systems – a capability that was nearly impossible before Presto/Trino.

Presto’s performance relies heavily on sophisticated memory management:

• In-Memory Processing: Data is processed in memory whenever possible
• Adaptive Execution: Adjusts query plans based on runtime statistics
• Pipelined Execution: Begins producing results before all data is processed
• Memory Pooling: Tracks and limits memory usage per query
• Spill-to-Disk: Gracefully handles situations when memory is insufficient

This architecture enables Presto to execute complex analytical queries against petabytes of data in seconds or minutes, rather than hours or days.

The ability to join data across disparate sources is revolutionary:

-- Join data from a data lake with a relational database
SELECT
    h.product_id,
    h.total_views,
    p.name,
    p.category,
    p.price
FROM
    hive.analytics.product_views h
JOIN
    mysql.products.product_details p
ON h.product_id = p.id
WHERE
    h.view_date = CURRENT_DATE - INTERVAL '1' DAY
ORDER BY
    h.total_views DESC
LIMIT 100;

This capability eliminates painful ETL processes and data duplication, allowing analysts to query data where it resides.

Presto supports a rich subset of ANSI SQL, including:

• Complex joins (inner, outer, cross, semi)
• Window functions
• Subqueries
• Common table expressions (CTEs)
• User-defined functions
• Complex aggregations
• JSON functions
• Geospatial functions

This compatibility means teams can leverage existing SQL skills without learning specialized query languages.

The query optimizer makes intelligent decisions to execute queries efficiently:

• Reorders joins for optimal performance
• Pushes predicates to data sources
• Uses statistics to guide execution paths
• Performs dynamic filtering
• Optimizes partition pruning

These optimizations happen automatically, saving developers from manual performance tuning.

Presto clusters can scale horizontally with near-linear performance improvements:

• Add worker nodes to handle larger workloads
• Scale compute independently from storage
• Dynamic resource allocation
• Fault tolerance with automatic retry
• Node rebalancing for optimal resource utilization

Unlike batch-oriented systems, Presto enables real-time exploration:

• Interactive query times (seconds to minutes)
• Result streaming for immediate feedback
• Query cancellation for faster iteration
• Session-based property configuration

Organizations with large data lakes use Presto/Trino to unlock insights:

• Analyze log data stored in S3/HDFS
• Query IoT device telemetry
• Process customer event data
• Analyze website/app user behavior

Example: Airbnb uses Trino to query their 100+ PB data lake, allowing analysts to explore booking patterns, pricing optimization, and user experience data across billions of events.

Companies implementing data mesh architectures leverage Presto for cross-domain queries:

• Join data across organizational boundaries
• Maintain domain ownership of data
• Avoid centralized data copying
• Enable self-service analytics

Example: A large retail organization uses Trino to create a virtual layer across domain-owned data products, allowing business analysts to join inventory data with marketing campaigns and sales transactions without moving data.

Presto serves as a high-performance query layer beneath visualization tools:

• Connects to Tableau, Power BI, Looker, etc.
• Provides fast responses for interactive dashboards
• Enables exploration of raw data
• Reduces data duplication

Example: Comcast uses Presto to power dashboards that monitor network performance, customer experience, and content consumption patterns across petabytes of data.

Organizations with complex infrastructure leverage Presto’s flexibility:

• Query across on-premises and cloud data
• Bridge multiple cloud providers
• Migrate data without disrupting analytics
• Optimize costs between storage options

Example: A financial services company uses Trino to enable querying both sensitive on-premises data and cloud-based analytics data in a unified way, maintaining regulatory compliance while leveraging cloud economics.

Setting up a basic cluster is surprisingly straightforward:

# Download the Trino server
wget https://repo1.maven.org/maven2/io/trino/trino-server/373/trino-server-373.tar.gz

# Extract the archive
tar -xzf trino-server-373.tar.gz

# Create configuration directories
mkdir -p trino-server-373/etc/catalog

# Create node.properties
cat > trino-server-373/etc/node.properties << EOF
node.environment=production
node.id=ffffffff-ffff-ffff-ffff-ffffffffffff
node.data-dir=/var/trino/data
EOF

# Create JVM config
cat > trino-server-373/etc/jvm.config << EOF
-server
-Xmx16G
-XX:+UseG1GC
-XX:G1HeapRegionSize=32M
-XX:+UseGCOverheadLimit
-XX:+ExplicitGCInvokesConcurrent
-XX:+HeapDumpOnOutOfMemoryError
-XX:+ExitOnOutOfMemoryError
EOF

# Create config.properties for coordinator
cat > trino-server-373/etc/config.properties << EOF
coordinator=true
node-scheduler.include-coordinator=false
http-server.http.port=8080
query.max-memory=50GB
query.max-memory-per-node=1GB
discovery-server.enabled=true
discovery.uri=http://coordinator:8080
EOF

The worker configuration is similar, with a few key differences:

# Create config.properties for workers
cat > trino-server-373/etc/config.properties << EOF
coordinator=false
http-server.http.port=8080
query.max-memory-per-node=1GB
discovery.uri=http://coordinator:8080
EOF

For example, to connect to a Hive metastore:

# Create hive.properties in the catalog directory
cat > trino-server-373/etc/catalog/hive.properties << EOF
connector.name=hive
hive.metastore.uri=thrift://hive-metastore:9083
hive.storage-format=PARQUET
hive.compression-codec=SNAPPY
EOF

# Start the coordinator and workers
trino-server-373/bin/launcher start

Using the command-line client:

# Download the client
wget https://repo1.maven.org/maven2/io/trino/trino-cli/373/trino-cli-373-executable.jar
mv trino-cli-373-executable.jar trino
chmod +x trino

# Connect to the cluster
./trino --server localhost:8080 --catalog hive --schema default

# Run a query
trino> SELECT * FROM customer_data LIMIT 10;

Choose the right file formats for optimal performance:

• Columnar Formats: Parquet and ORC significantly outperform row-based formats like CSV
• Compression: Use Snappy or Zstd for a good balance of compression ratio and CPU usage
• Statistics: Ensure your files contain proper statistics for partition pruning
• File Size: Aim for files between 100MB-1GB to optimize parallelism

Effective partitioning dramatically improves query performance:

-- Well-partitioned table example
CREATE TABLE sales_data (
    transaction_id VARCHAR,
    product_id VARCHAR,
    amount DECIMAL,
    transaction_date DATE
)
WITH (
    partitioned_by = ARRAY['transaction_date'],
    format = 'PARQUET'
);

• Partition on commonly filtered columns
• Avoid over-partitioning (thousands of tiny partitions)
• Consider multi-level partitioning for very large tables

Experienced Presto users leverage these techniques:

• Use EXPLAIN to understand query execution plans
• Filter early to reduce data scanning
• Leverage approximate functions for faster results (approx_distinct vs. count(distinct))
• Use LIMIT when exploring large datasets
• Optimize JOIN order for tables with vastly different sizes
• Leverage window functions instead of self-joins when possible

For multi-tenant environments:

• Implement query queuing with resource groups
• Set appropriate memory limits per query
• Configure CPU priorities for different workloads
• Establish query timeout policies
• Monitor query patterns to identify optimization opportunities

While both support SQL on Hadoop:

• Performance: Presto is significantly faster for interactive queries
• Use Case: Hive excels at batch processing, Presto at interactive analytics
• Resources: Presto uses more memory, Hive is more disk-oriented
• SQL Support: Presto offers more comprehensive SQL compatibility

Comparing two popular SQL-on-big-data solutions:

• Architecture: Spark is a general-purpose compute engine with SQL capabilities; Presto is SQL-first
• Performance: Presto typically faster for interactive queries; Spark better for complex, long-running jobs
• Integration: Spark offers tight integration with ML and streaming; Presto focuses on SQL federation
• Resources: Spark has higher startup overhead but can leverage disk more effectively

Comparing to managed cloud data warehouses:

• Deployment: Presto requires self-management; BigQuery/Snowflake are fully managed
• Cost Model: Presto has upfront infrastructure costs; cloud warehouses charge for compute/storage usage
• Federation: Presto excels at querying diverse sources; cloud warehouses typically require data ingestion
• Performance: Cloud warehouses often offer better performance guarantees and optimization

The Presto/Trino ecosystem continues to evolve rapidly:

Modern table formats are bringing ACID transactions and advanced features to data lakes:

-- Creating a Trino table using Iceberg
CREATE TABLE iceberg.analytics.customer_events (
    user_id VARCHAR,
    event_type VARCHAR,
    event_time TIMESTAMP,
    properties MAP(VARCHAR, VARCHAR)
)
WITH (
    format = 'PARQUET',
    partitioning = ARRAY['day(event_time)']
);

These integrations enable:

• Time travel queries
• Schema evolution
• ACID transactions on data lakes
• Improved metadata management

Enterprise adoption is driving security improvements:

• Row-level and column-level security
• Dynamic data masking
• Integration with identity providers
• Comprehensive audit logging
• Encryption for data-in-transit and data-at-rest

Organizations are seeking better cost management:

• Query cost estimation
• Resource usage tracking
• Workload-aware autoscaling
• Cost-based query routing
• Intelligent caching mechanisms

The trend toward serverless is impacting Presto deployments:

• Kubernetes-native scaling
• Rapid worker provisioning
• Pay-per-query models
• Separation of storage and compute costs
• Auto-suspend/resume capabilities

Presto/Trino shines brightest in these scenarios:

• Data Federation Needs: Organizations with data spread across multiple systems
• Interactive Analytics: Teams requiring sub-minute query responses on large datasets
• SQL Skills Leverage: Companies with strong SQL expertise seeking to apply it to big data
• Cost Optimization: Organizations looking to query data in place without expensive movement
• Hybrid Architectures: Enterprises with both on-premises and cloud data

However, it may not be ideal for:

• Small datasets where traditional databases excel
• Extremely complex ETL workflows (though this is improving)
• Organizations without infrastructure expertise (unless using a managed service)
• Use cases requiring sub-second response times for all queries

For many modern data-driven organizations, Presto/Trino has become an indispensable component of their analytics architecture, delivering on the promise of interactive SQL queries across their entire data estate. As data volumes continue to grow and diversify, the value proposition of this revolutionary query engine only becomes stronger.

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