H2O DB Benchmark

CLI name: h2odb - use benchbox run --benchmark h2odb

Overview

The H2O DB Benchmark is designed to test analytical database performance using real-world taxi trip data patterns. Originally developed by H2O.ai for their database benchmarking initiative, this benchmark focuses on fundamental analytical operations that are common in data science and machine learning workflows: aggregations, grouping operations, and time-series analysis.

The benchmark is particularly valuable for testing systems used in data science pipelines, as it models the types of exploratory data analysis and feature engineering operations that data scientists perform regularly on large datasets.

Key Features

Real-world data patterns - Based on NYC taxi trip data structure
Data science focus - Tests operations common in ML pipelines
Single-table design - Emphasizes aggregation and grouping performance
Time-series operations - Tests temporal aggregation patterns
Scalable testing - Configurable data sizes from MB to TB scale
Analytics-oriented queries - Focuses on data exploration patterns
Performance measurement - Designed for precise timing comparisons

BenchBox generates synthetic taxi trip data that mimics the structure and characteristics of the NYC Taxi & Limousine Commission Trip Record Data used in the H2O DB benchmark.

Schema Description

The H2O DB benchmark uses a single table design based on the NYC Taxi & Limousine Commission Trip Record Data structure:

Core Table

Table	Purpose	Approximate Rows (SF 1)
TRIPS	Taxi trip records with fare and location data	10,000,000

Schema Details

TRIPS Table Structure:

Column	Type	Description
`vendor_id`	INTEGER	Taxi vendor identifier
`pickup_datetime`	TIMESTAMP	Trip start timestamp
`dropoff_datetime`	TIMESTAMP	Trip end timestamp
`passenger_count`	INTEGER	Number of passengers
`trip_distance`	DECIMAL(8,2)	Trip distance in miles
`pickup_longitude`	DECIMAL(18,14)	Pickup location longitude
`pickup_latitude`	DECIMAL(18,14)	Pickup location latitude
`rate_code_id`	INTEGER	Rate code for the trip
`store_and_fwd_flag`	VARCHAR(1)	Whether trip was stored before transmission
`dropoff_longitude`	DECIMAL(18,14)	Dropoff location longitude
`dropoff_latitude`	DECIMAL(18,14)	Dropoff location latitude
`payment_type`	INTEGER	Payment method used
`fare_amount`	DECIMAL(8,2)	Base fare amount
`extra`	DECIMAL(8,2)	Extra charges
`mta_tax`	DECIMAL(8,2)	Metropolitan Transportation Authority tax
`tip_amount`	DECIMAL(8,2)	Tip amount
`tolls_amount`	DECIMAL(8,2)	Tolls paid
`improvement_surcharge`	DECIMAL(8,2)	Improvement surcharge
`total_amount`	DECIMAL(8,2)	Total trip cost
`pickup_location_id`	INTEGER	Pickup location zone ID
`dropoff_location_id`	INTEGER	Dropoff location zone ID
`congestion_surcharge`	DECIMAL(8,2)	Congestion pricing surcharge

Data Characteristics

The TRIPS table contains realistic data distributions that reflect actual taxi usage patterns:

Temporal patterns: Peak hours, seasonal variations, weekday/weekend differences
Geographic clusters: High-density pickup/dropoff areas (airports, Manhattan, etc.)
Fare distributions: Realistic fare amounts, tip percentages, and surcharges
Passenger patterns: Typical passenger counts and trip distances

Query Characteristics

The H2O DB benchmark includes analytical queries that test different aspects of database performance.

Each query has a [query template](queries/h2odb/index.md) with a representative
SQL and DataFrame rendering, plus how to extract the exact statement for your
platform and scale.

Basic Aggregation Queries

Q1: Simple Count

SELECT COUNT(*) as count
FROM trips;

Purpose: Test basic table scanning and counting
Performance focus: Sequential scan optimization

Q2: Sum and Mean

SELECT
    SUM(fare_amount) as sum_fare_amount,
    AVG(fare_amount) as mean_fare_amount
FROM trips;

Purpose: Test numeric aggregation functions
Performance focus: Aggregation algorithm efficiency

Grouping Queries

Q3: Single-Column Grouping

SELECT
    passenger_count,
    SUM(fare_amount) as sum_fare_amount
FROM trips
GROUP BY passenger_count
ORDER BY passenger_count;

Purpose: Test basic GROUP BY performance
Performance focus: Hash aggregation vs. sort-based grouping

Q4: Multi-Aggregate Grouping

SELECT
    passenger_count,
    SUM(fare_amount) as sum_fare_amount,
    AVG(fare_amount) as mean_fare_amount
FROM trips
GROUP BY passenger_count
ORDER BY passenger_count;

Purpose: Test multiple aggregation functions in single query
Performance focus: Aggregation pipeline efficiency

Q5: Two-Column Grouping

SELECT
    passenger_count,
    vendor_id,
    SUM(fare_amount) as sum_fare_amount
FROM trips
GROUP BY passenger_count, vendor_id
ORDER BY passenger_count, vendor_id;

Purpose: Test multi-column grouping performance
Performance focus: Hash table size and collision handling

Temporal Analysis Queries

Q7: Time-Based Grouping

SELECT
    EXTRACT(HOUR FROM pickup_datetime) as hour,
    SUM(fare_amount) as sum_fare_amount
FROM trips
GROUP BY EXTRACT(HOUR FROM pickup_datetime)
ORDER BY hour;

Purpose: Test date/time function performance
Performance focus: Temporal extraction and grouping

Q8: Complex Temporal Analysis

SELECT
    DATE_TRUNC('day', pickup_datetime) as pickup_date,
    passenger_count,
    COUNT(*) as trip_count,
    AVG(fare_amount) as avg_fare,
    AVG(trip_distance) as avg_distance
FROM trips
WHERE pickup_datetime >= '{start_date}'
  AND pickup_datetime < '{end_date}'
GROUP BY DATE_TRUNC('day', pickup_datetime), passenger_count
ORDER BY pickup_date, passenger_count;

Purpose: Test complex temporal aggregation with filtering
Performance focus: Date range filtering and multi-level grouping

Advanced-level Analytics Queries

Q9: Statistical Analysis

SELECT
    vendor_id,
    COUNT(*) as trip_count,
    SUM(fare_amount) as total_revenue,
    AVG(fare_amount) as avg_fare,
    STDDEV(fare_amount) as fare_stddev,
    MIN(fare_amount) as min_fare,
    MAX(fare_amount) as max_fare,
    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY fare_amount) as median_fare
FROM trips
WHERE fare_amount > 0
GROUP BY vendor_id
ORDER BY total_revenue DESC;

Purpose: Test statistical function performance
Performance focus: Percentile calculations and complex aggregations

Usage Examples

Basic Benchmark Setup

from benchbox import H2ODB

h2odb = H2ODB(scale_factor=1.0, output_dir="h2odb_data")

data_files = h2odb.generate_data()

queries = h2odb.get_queries()
print(f"Generated {len(queries)} H2O DB queries")

count_query = h2odb.get_query("Q1")
print(count_query)

Data Generation at Scale

h2odb_large = H2ODB(scale_factor=10.0, output_dir="h2odb_large")
data_files = h2odb_large.generate_data()

trips_file = h2odb_large.output_dir / "trips.csv"
size_gb = trips_file.stat().st_size / (1024**3)
print(f"Generated trips data: {size_gb:.2f} GB")

DuckDB Integration Example

import duckdb
from benchbox import H2ODB

h2odb = H2ODB(scale_factor=0.1, output_dir="h2odb_small")
data_files = h2odb.generate_data()

conn = duckdb.connect("h2odb.duckdb")
schema_sql = h2odb.get_create_tables_sql()
conn.execute(schema_sql)

trips_file = h2odb.output_dir / "trips.csv"
conn.execute(f"""
    INSERT INTO trips
    SELECT * FROM read_csv('{trips_file}',
                          header=true,
                          auto_detect=true)
""")

row_count = conn.execute("SELECT COUNT(*) FROM trips").fetchone()[0]
print(f"Loaded {row_count:,} trip records")

query_results = {}

for query_id in ["Q1", "Q2", "Q3", "Q4", "Q5", "Q6"]:
    query_sql = h2odb.get_query(query_id)
    result = conn.execute(query_sql).fetchall()
    query_results[query_id] = len(result)
    print(f"{query_id}: {len(result)} result rows")

temporal_params = {
    'start_date': '2020-01-01',
    'end_date': '2020-01-31'
}

for query_id in ["Q7", "Q8"]:
    query_sql = h2odb.get_query(query_id, params=temporal_params)
    result = conn.execute(query_sql).fetchall()
    query_results[query_id] = len(result)
    print(f"{query_id}: {len(result)} result rows")

conn.close()

Queries Q1 to Q6 are the basic aggregation tests. Q7 and Q8 are temporal analysis queries and take start and end dates as parameters.

Performance Benchmarking Framework

import time
from typing import Dict, List
from statistics import mean, median

class H2ODBPerformanceTester:
    def __init__(self, h2odb: H2ODB, connection):
        self.h2odb = h2odb
        self.connection = connection

    def benchmark_query_group(self, query_group: str, iterations: int = 3) -> Dict:
        query_groups = {
            'basic': ['Q1', 'Q2'],
            'grouping': ['Q3', 'Q4', 'Q5', 'Q6'],
            'temporal': ['Q7', 'Q8'],
            'advanced': ['Q9', 'Q10']
        }

        if query_group not in query_groups:
            raise ValueError(f"Invalid query group: {query_group}")

        query_ids = query_groups[query_group]
        results = {}

        params = {
            'start_date': '2020-01-01',
            'end_date': '2020-01-31',
            'min_fare': 5.0,
            'max_fare': 100.0
        }

        for query_id in query_ids:
            print(f"Benchmarking {query_id} ({query_group})...")

            times = []
            for iteration in range(iterations):
                query_sql = self.h2odb.get_query(query_id, params=params)

                start_time = time.time()
                result = self.connection.execute(query_sql).fetchall()
                execution_time = time.time() - start_time

                times.append(execution_time)
                print(f"  Iteration {iteration + 1}: {execution_time:.3f}s")

            results[query_id] = {
                'group': query_group,
                'avg_time': mean(times),
                'median_time': median(times),
                'min_time': min(times),
                'max_time': max(times),
                'rows_returned': len(result),
                'times': times
            }

        return results

    def run_complete_benchmark(self) -> Dict:
        complete_results = {}

        for group in ['basic', 'grouping', 'temporal', 'advanced']:
            print(f"\\nRunning {group.upper()} queries...")
            try:
                group_results = self.benchmark_query_group(group)
                complete_results[group] = group_results
            except Exception as e:
                print(f"Error in {group} queries: {e}")
                complete_results[group] = {'error': str(e)}

        all_times = []
        for group_data in complete_results.values():
            if isinstance(group_data, dict) and 'error' not in group_data:
                for query_data in group_data.values():
                    if isinstance(query_data, dict) and 'times' in query_data:
                        all_times.extend(query_data['times'])

        if all_times:
            complete_results['summary'] = {
                'total_queries': len(all_times) // 3,
                'total_avg_time': mean(all_times),
                'total_median_time': median(all_times),
                'total_min_time': min(all_times),
                'total_max_time': max(all_times)
            }

        return complete_results

    def analyze_aggregation_performance(self) -> Dict:
        aggregation_tests = [
            ('single_agg', 'SELECT passenger_count, SUM(fare_amount) FROM trips GROUP BY passenger_count'),
            ('multi_agg', 'SELECT passenger_count, SUM(fare_amount), AVG(fare_amount), COUNT(*) FROM trips GROUP BY passenger_count'),
            ('two_col_group', 'SELECT passenger_count, vendor_id, SUM(fare_amount) FROM trips GROUP BY passenger_count, vendor_id'),
            ('complex_agg', 'SELECT vendor_id, COUNT(*), SUM(fare_amount), AVG(fare_amount), STDDEV(fare_amount) FROM trips GROUP BY vendor_id')
        ]

        results = {}

        for test_name, query_sql in aggregation_tests:
            print(f"Testing {test_name}...")

            times = []
            for iteration in range(3):
                start_time = time.time()
                result = self.connection.execute(query_sql).fetchall()
                execution_time = time.time() - start_time
                times.append(execution_time)

            results[test_name] = {
                'avg_time': mean(times),
                'rows_returned': len(result),
                'times': times
            }

        return results

performance_tester = H2ODBPerformanceTester(h2odb, conn)

basic_results = performance_tester.benchmark_query_group('basic')
grouping_results = performance_tester.benchmark_query_group('grouping')

print("\\nQuery Group Performance Summary:")
print(f"Basic Queries: {basic_results}")
print(f"Grouping Queries: {grouping_results}")

complete_results = performance_tester.run_complete_benchmark()
print(f"\\nComplete Benchmark Summary: {complete_results.get('summary', 'No summary available')}")

agg_results = performance_tester.analyze_aggregation_performance()
print(f"\\nAggregation Performance Analysis: {agg_results}")

The total_queries summary divides the number of timings by 3 because each query runs 3 iterations by default.

Data Science Workflow Integration

import pandas as pd
from benchbox import H2ODB

h2odb = H2ODB(scale_factor=1.0, output_dir="h2odb_ds")
data_files = h2odb.generate_data()

trips_df = pd.read_csv(h2odb.output_dir / "trips.csv")

print("Data Science Operations Performance Test:")

start_time = time.time()
trips_df['hour'] = pd.to_datetime(trips_df['pickup_datetime']).dt.hour
trips_df['day_of_week'] = pd.to_datetime(trips_df['pickup_datetime']).dt.dayofweek
trips_df['trip_duration'] = (pd.to_datetime(trips_df['dropoff_datetime']) -
                            pd.to_datetime(trips_df['pickup_datetime'])).dt.total_seconds()
feature_eng_time = time.time() - start_time
print(f"Feature engineering: {feature_eng_time:.3f}s")

start_time = time.time()
hourly_stats = trips_df.groupby('hour').agg({
    'fare_amount': ['sum', 'mean', 'std', 'count'],
    'trip_distance': ['mean'],
    'passenger_count': ['mean']
}).round(2)
groupby_time = time.time() - start_time
print(f"GroupBy aggregation: {groupby_time:.3f}s")

start_time = time.time()
vendor_stats = trips_df.groupby('vendor_id').agg({
    'fare_amount': ['count', 'sum', 'mean', 'std', 'min', 'max'],
    'tip_amount': ['mean', 'std'],
    'trip_distance': ['mean', 'std']
}).round(2)
stats_time = time.time() - start_time
print(f"Statistical analysis: {stats_time:.3f}s")

print(f"\\nTotal preprocessing time: {feature_eng_time + groupby_time + stats_time:.3f}s")

The example loads the data into pandas to simulate ML preprocessing, then times three typical data science operations that H2O DB tests: feature engineering, GroupBy aggregation (similar to the H2O DB queries) and statistical analysis.

Performance Characteristics

Query Performance Patterns

Basic Queries (Q1-Q2):

Primary bottleneck: Sequential scan speed and basic aggregation
Optimization targets: Column store scanning, SIMD operations
Typical performance: Sub-second (10ms-2s depending on data size)
Scaling characteristics: Linear with data size

Grouping Queries (Q3-Q6):

Primary bottleneck: Hash table construction and aggregation algorithms
Optimization targets: Hash aggregation efficiency, memory management
Typical performance: Fast to medium, seconds to tens of seconds
Scaling characteristics: Sub-linear with good hash distribution

Temporal Queries (Q7-Q8):

Primary bottleneck: Date/time function evaluation and temporal grouping
Optimization targets: Date extraction optimization, temporal indexing
Typical performance: Medium, seconds to minutes
Scaling characteristics: Depends on temporal selectivity

Advanced-level Analytics (Q9-Q10):

Primary bottleneck: Statistical function computation, complex aggregations
Optimization targets: Statistical algorithm efficiency, sorting performance
Typical performance: Slower, minutes for large datasets
Scaling characteristics: Often super-linear for percentile calculations

System Optimization Opportunities

System Type	Basic Queries	Grouping Queries	Temporal Queries	Analytics Queries
Columnar Stores	Column scanning	Column-wise hash tables	Date column optimization	Vectorized statistics
Row Stores	Index scanning	Row-wise aggregation	Temporal indexes	Row-wise computation
In-Memory	SIMD operations	In-memory hash tables	Fast date functions	In-memory sorting
GPU-Accelerated	Parallel scanning	GPU hash aggregation	GPU date operations	GPU statistical functions

Configuration Options

Scale Factor Guidelines

Scale Factor	Rows	Data Size	Memory Usage	Use Case
0.01	~100K	~10 MB	< 100 MB	Development
0.1	~1M	~100 MB	< 500 MB	Testing
1.0	~10M	~1 GB	< 4 GB	Standard benchmark
10.0	~100M	~10 GB	< 20 GB	Performance testing
100.0	~1B	~100 GB	< 100 GB	Large-scale testing
1000.0	~10B	~1 TB	< 500 GB	Production simulation

Advanced-level Configuration

h2odb = H2ODB(
    scale_factor=1.0,
    output_dir="h2odb_data",
    date_range_days=365,
    trip_distance_max=50.0,
    fare_amount_max=200.0,
    enable_indexing=True,
    partition_by_date=True,
    compress_output=True
)

date_range_days: range of trip dates.
trip_distance_max: maximum trip distance.
fare_amount_max: maximum fare amount.
enable_indexing: create performance indices.
partition_by_date: partition by pickup date.
compress_output: compress generated files.

Integration Examples

Apache Spark Integration

from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from benchbox import H2ODB

spark = SparkSession.builder \
    .appName("H2ODB-Benchmark") \
    .config("spark.sql.adaptive.enabled", "true") \
    .config("spark.sql.adaptive.coalescePartitions.enabled", "true") \
    .getOrCreate()

h2odb = H2ODB(scale_factor=100, output_dir="/data/h2odb_sf100")
data_files = h2odb.generate_data()

trips_df = spark.read.csv("/data/h2odb_sf100/trips.csv",
                         header=True, inferSchema=True)

trips_df = trips_df.withColumn("pickup_date",
                               to_date("pickup_datetime"))
trips_df = trips_df.repartition(100, "pickup_date")
trips_df.cache()
trips_df.createOrReplaceTempView("trips")

print("Running H2O DB queries on Spark...")

q1_result = spark.sql("SELECT COUNT(*) as count FROM trips")
q1_result.show()

q3_result = spark.sql("""
    SELECT
        passenger_count,
        SUM(fare_amount) as sum_fare_amount
    FROM trips
    GROUP BY passenger_count
    ORDER BY passenger_count
""")
q3_result.show()

q7_result = spark.sql("""
    SELECT
        hour(pickup_datetime) as hour,
        SUM(fare_amount) as sum_fare_amount
    FROM trips
    GROUP BY hour(pickup_datetime)
    ORDER BY hour
""")
q7_result.show()

q3_result.explain(True)

spark.stop()

The example partitions the data by pickup date for temporal queries and caches it. Q1 is a basic count, Q3 groups by passenger count, and Q7 is a temporal analysis by hour. explain(True) shows the execution plan for Q3.

ClickHouse Integration

import clickhouse_connect
from benchbox import H2ODB

client = clickhouse_connect.get_client(host='localhost', port=8123)
h2odb = H2ODB(scale_factor=10.0, output_dir="h2odb_data")

data_files = h2odb.generate_data()

create_table_sql = """
CREATE TABLE trips (
    vendor_id UInt8,
    pickup_datetime DateTime,
    dropoff_datetime DateTime,
    passenger_count UInt8,
    trip_distance Decimal(8,2),
    pickup_longitude Decimal(18,14),
    pickup_latitude Decimal(18,14),
    rate_code_id UInt8,
    store_and_fwd_flag String,
    dropoff_longitude Decimal(18,14),
    dropoff_latitude Decimal(18,14),
    payment_type UInt8,
    fare_amount Decimal(8,2),
    extra Decimal(8,2),
    mta_tax Decimal(8,2),
    tip_amount Decimal(8,2),
    tolls_amount Decimal(8,2),
    improvement_surcharge Decimal(8,2),
    total_amount Decimal(8,2),
    pickup_location_id UInt16,
    dropoff_location_id UInt16,
    congestion_surcharge Decimal(8,2)
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(pickup_datetime)
ORDER BY (pickup_datetime, vendor_id);
"""

client.execute(create_table_sql)

trips_file = h2odb.output_dir / "trips.csv"
with open(trips_file, 'rb') as f:
    client.insert_file('trips', f, fmt='CSV')

print("Running H2O DB queries on ClickHouse...")

q1_configured = "SELECT count() FROM trips"
q1_result = client.query(q1_configured)
print(f"Q1 result: {q1_result.result_rows}")

q3_configured = """
SELECT
    passenger_count,
    sum(fare_amount) as sum_fare_amount
FROM trips
GROUP BY passenger_count
ORDER BY passenger_count
"""
q3_result = client.query(q3_configured)
print(f"Q3 results: {len(q3_result.result_rows)} groups")

q7_configured = """
SELECT
    toHour(pickup_datetime) as hour,
    sum(fare_amount) as sum_fare_amount
FROM trips
GROUP BY toHour(pickup_datetime)
ORDER BY hour
"""
q7_result = client.query(q7_configured)
print(f"Q7 results: {len(q7_result.result_rows)} hours")

The table uses a MergeTree engine partitioned by month, and the queries use ClickHouse-native functions (count(), sum(), toHour()) for Q1 (count), Q3 (grouping) and Q7 (temporal analysis). The CSV is loaded with ClickHouse's CSV import.

Best Practices

Data Generation

Scale appropriately - Use realistic scale factors for your system capacity
Consider temporal distribution - Generate realistic date ranges
Optimize data types - Use appropriate precision for decimal fields
Partition strategy - Partition by date for temporal query performance

Query Optimization

Index strategy - Create indices on frequently grouped columns
Aggregation optimization - Use hash aggregation where possible
Memory management - Monitor memory usage for large grouping operations
Parallel execution - Use parallel aggregation for large datasets

Performance Testing

Warm-up queries - Run queries multiple times to account for caching
Resource monitoring - Monitor CPU, memory, and I/O during execution
Baseline establishment - Establish performance baselines for regression testing
Statistical validation - Use multiple iterations for reliable timing

Common Issues and Solutions

Performance Issues

Issue: Slow aggregation queries on large datasets

Solution: create appropriate indices and use columnar storage.

CREATE INDEX idx_trips_passenger_count ON trips(passenger_count);
CREATE INDEX idx_trips_vendor_id ON trips(vendor_id);
CREATE INDEX idx_trips_pickup_hour ON trips(EXTRACT(HOUR FROM pickup_datetime));

CREATE TABLE trips_columnar (
    LIKE trips
) USING PARQUET
PARTITIONED BY (DATE_TRUNC('month', pickup_datetime));

Issue: Memory issues with large GROUP BY operations

Solution: use incremental aggregation or external sorting. The first SET statement is for PostgreSQL and the second is for ClickHouse. Alternatively, break large groups into smaller pieces, such as the monthly chunk below.

SET work_mem = '2GB';
SET max_memory_usage = 8000000000;

SELECT passenger_count, vendor_id, SUM(fare_amount)
FROM trips
WHERE pickup_datetime >= '2020-01-01'
  AND pickup_datetime < '2020-02-01'
GROUP BY passenger_count, vendor_id;

Data Quality Issues

Issue: Incorrect temporal analysis results

Solution: ensure proper timezone handling and date parsing. The pickup_datetime >= '1900-01-01' filter removes invalid dates.

SELECT
    EXTRACT(HOUR FROM pickup_datetime AT TIME ZONE 'UTC') as hour,
    SUM(fare_amount) as sum_fare_amount
FROM trips
WHERE pickup_datetime IS NOT NULL
  AND pickup_datetime >= '1900-01-01'
GROUP BY EXTRACT(HOUR FROM pickup_datetime AT TIME ZONE 'UTC')
ORDER BY hour;

Issue: Unexpected aggregation results

Solution: handle NULL values and outliers appropriately. The passenger_count BETWEEN 0 AND 10 filter removes unrealistic values.

SELECT
    passenger_count,
    COUNT(*) as trip_count,
    SUM(CASE WHEN fare_amount > 0 THEN fare_amount ELSE 0 END) as sum_fare_amount,
    AVG(CASE WHEN fare_amount BETWEEN 0 AND 1000 THEN fare_amount END) as avg_fare_amount
FROM trips
WHERE passenger_count BETWEEN 0 AND 10
GROUP BY passenger_count
ORDER BY passenger_count;

External Resources

H2O.ai DB Benchmark - Original benchmark specification
NYC Taxi Data - Source data format
Database Performance Analysis - Performance comparisons
Data Science Pipeline Optimization - H2O.ai data science resources

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h2odb.md

h2odb.md

H2O DB Benchmark

Overview

Key Features

Schema Description

Core Table

Schema Details

Data Characteristics

Query Characteristics

Basic Aggregation Queries

Grouping Queries

Temporal Analysis Queries

Advanced-level Analytics Queries

Usage Examples

Basic Benchmark Setup

Data Generation at Scale

DuckDB Integration Example

Performance Benchmarking Framework

Data Science Workflow Integration

Performance Characteristics

Query Performance Patterns

System Optimization Opportunities

Configuration Options

Scale Factor Guidelines

Advanced-level Configuration

Integration Examples

Apache Spark Integration

ClickHouse Integration

Best Practices

Data Generation

Query Optimization

Performance Testing

Common Issues and Solutions

Performance Issues

Data Quality Issues

Related Documentation

External Resources

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File metadata and controls

H2O DB Benchmark

Overview

Key Features

Schema Description

Core Table

Schema Details

Data Characteristics

Query Characteristics

Basic Aggregation Queries

Grouping Queries

Temporal Analysis Queries

Advanced-level Analytics Queries

Usage Examples

Basic Benchmark Setup

Data Generation at Scale

DuckDB Integration Example

Performance Benchmarking Framework

Data Science Workflow Integration

Performance Characteristics

Query Performance Patterns

System Optimization Opportunities

Configuration Options

Scale Factor Guidelines

Advanced-level Configuration

Integration Examples

Apache Spark Integration

ClickHouse Integration

Best Practices

Data Generation

Query Optimization

Performance Testing

Common Issues and Solutions

Performance Issues

Data Quality Issues

Related Documentation

External Resources