Wouldn't it be amazing if the world operated on a single timezone (without Daylight Savings Time)? Sure would make life easier for software developers and database admins.

Computing UTC offsets that account for daylight saving time transitions can make or break time-sensitive applications, especially when users span multiple timezones. ClickHouse^®'s timeZoneOffset() function automatically handles these complexities by leveraging the IANA timezone database to calculate precise second-based offsets for any datetime value.

This guide walks through the function's syntax, performance considerations, and practical examples—from basic offset calculations to building timezone-aware APIs that serve real-time data across global applications.

Understanding timeZoneOffset() and UTC offsets

The timeZoneOffset() function in ClickHouse^® calculates the difference in seconds between a local time and UTC, automatically handling daylight saving time transitions. This function takes a DateTime value with timezone information and returns the offset as an integer representing seconds—negative values indicate time zones behind UTC, while positive values represent zones ahead.

Think of it this way: when it's noon in New York during winter, UTC shows 5:00 PM. The timeZoneOffset() function returns -18000 seconds (negative 5 hours) to represent this difference. During summer when DST kicks in, the same noon in New York corresponds to 4:00 PM UTC, so the function returns -14400 seconds instead.

What problem does it solve?

When working with timestamps across different regions, you often encounter the challenge of calculating precise time differences that account for DST rules changing throughout the year. Without proper DST handling, your calculations can be off by an hour during transition periods, leading to incorrect analytics and reporting.

Consider a scenario where you're analyzing user activity patterns across time zones. If your offset calculations don't account for DST transitions, March and November data might show artificial spikes or dips that don't reflect actual user behavior.

How DST is accounted for

ClickHouse^® uses the IANA timezone database to automatically apply correct DST rules for any given date and location. The database contains historical DST changes and future scheduled transitions, so timeZoneOffset() produces accurate results whether you're analyzing data from 2010 or projecting into 2030.

The function doesn't just apply a static offset—it actually looks up the specific DST rules for the timezone and date you're querying. This means the same timezone can produce different offsets depending on the time of year, all handled automatically behind the scenes.

Quick start example: return offset in seconds

Here's the most direct way to compute a UTC offset in seconds using timeZoneOffset():

SELECT
    toDateTime('2023-01-15 12:00:00', 'America/New_York') AS winter_time,
    timeZoneOffset(winter_time) AS winter_offset,
    toDateTime('2023-07-15 12:00:00', 'America/New_York') AS summer_time,
    timeZoneOffset(summer_time) AS summer_offset

This query demonstrates how the same timezone produces different offsets depending on whether DST is active. You'll see -18000 for winter (UTC-5) and -14400 for summer (UTC-4).

Select a timestamp column

When working with existing data, you can apply timeZoneOffset() directly to DateTime columns:

SELECT
    event_timestamp,
    timeZoneOffset(event_timestamp) AS utc_offset_seconds
FROM user_events
WHERE toDate(event_timestamp) = '2023-06-15'

The function works with any DateTime column that has timezone metadata attached. If your column lacks timezone information, you'll get results based on the server's default timezone, which might not be what you want.

Cast the result to seconds

The timeZoneOffset() function already returns results in seconds by default—no additional casting is required. However, you might want to convert to other units for display purposes:

SELECT
    timeZoneOffset(timestamp_col) AS offset_seconds,
    timeZoneOffset(timestamp_col) / 3600 AS offset_hours
FROM your_table

Function signature and parameters

The complete syntax for timeZoneOffset() is straightforward: timeZoneOffset(datetime_expression). The function accepts a single parameter and returns an integer representing the UTC offset in seconds.

Arguments and defaults

The function requires one argument: a DateTime or DateTime64 value that includes timezone information. If your DateTime column doesn't have timezone metadata, you'll get results based on the server's default timezone setting.

You can also pass a timezone string as a second parameter: timeZoneOffset(datetime_value, 'Europe/London'). This approach proves useful when you want to calculate offsets for different timezones than what's stored in your data.

Supported data types

The function works with these input types:

• DateTime: Standard timestamp with timezone information
• DateTime64: High-precision timestamps with sub-second accuracy
• Timezone-aware variants: Any DateTime type that includes timezone metadata

String representations of dates won't work directly—you'll need to convert them to proper DateTime types first using functions like toDateTime() or parseDateTime().

Zone name resolution

ClickHouse^® follows IANA timezone naming conventions, so you'll use identifiers like 'America/New_York' or 'Europe/Berlin' rather than abbreviations like EST or CET. The system maintains a complete database of timezone rules, including historical changes and future scheduled transitions.

Abbreviations like PST or EST can be ambiguous because they don't account for DST transitions or regional variations. IANA names provide unambiguous timezone identification that works consistently across different time periods.

Handling DateTime64 and sub-second precision

When working with microsecond or nanosecond precision timestamps, timeZoneOffset() maintains the same behavior—it calculates the offset based on the date and timezone, not the sub-second components.

Why millisecond timestamps need extra care

DST transitions happen at specific moments (typically 2:00 AM), and high-precision timestamps can capture data right around these transition points. While the offset calculation itself doesn't depend on milliseconds, you might encounter edge cases where timestamps fall exactly during the "spring forward" or "fall back" periods.

During spring forward transitions, times between 2:00 AM and 3:00 AM don't exist in the local timezone. During fall back transitions, times between 1:00 AM and 2:00 AM occur twice. ClickHouse^® handles these cases according to standard timezone rules.

Sample query with DateTime64

Here's how to work with high-precision timestamps:

SELECT
    toDateTime64('2023-03-12 07:00:00.123456', 6, 'America/New_York') AS precise_time,
    timeZoneOffset(precise_time) AS offset_seconds,
    formatDateTime(precise_time, '%Y-%m-%d %H:%M:%S.%f') AS formatted

The offset calculation ignores the microsecond component but still produces accurate results based on the date and timezone information.

Choosing the right time zone source

You have several options for specifying timezone information when calculating offsets, each with different use cases and performance implications.

System default zone

When you don't specify a timezone, ClickHouse^® uses the server's default timezone setting. This can lead to inconsistent results if your server timezone differs from your data's actual timezone:

-- Uses server timezone (potentially incorrect)
SELECT timeZoneOffset(toDateTime('2023-06-15 12:00:00'))

This approach works best when all your data originates from the same timezone as your server. However, relying on server settings makes your queries less portable and harder to debug.

Explicit IANA zone

The most reliable approach involves explicitly specifying timezone strings:

-- Explicit timezone (recommended)
SELECT timeZoneOffset(toDateTime('2023-06-15 12:00:00', 'Europe/London'))

This method ensures consistent results regardless of server configuration changes. It also makes your queries self-documenting by clearly showing which timezone you're working with.

Zone in column metadata

If your DateTime columns already include timezone information, timeZoneOffset() uses that metadata automatically:

-- Assumes event_time column has timezone metadata
SELECT timeZoneOffset(event_time) FROM events

This approach works well when your table schema consistently stores timezone information with timestamp columns.

timeZoneOffset() versus toTimeZone()

The two functions serve different purposes in timezone handling, though they're often used together in complex queries.

When to use each function

Choose the right function based on what you need:

• timeZoneOffset(): When you need the numeric offset value for calculations, comparisons, or API responses
• toTimeZone(): When you need to convert timestamps for display in different zones or for timezone-aware filtering

The key difference is that timeZoneOffset() returns a number, while toTimeZone() returns a DateTime value in the target timezone. Think of offset as the "distance" from UTC, while conversion as "moving" the timestamp to a different timezone.

Side-by-side output example

Here's how the same input produces different outputs:

SELECT
    toDateTime('2023-06-15 12:00:00', 'America/New_York') AS original,
    timeZoneOffset(original) AS offset_seconds,
    toTimeZone(original, 'UTC') AS converted_utc,
    toTimeZone(original, 'Europe/London') AS converted_london

The offset tells you the relationship to UTC, while toTimeZone() shows you what the time looks like in different regions.

Performance considerations with large tables

When processing millions of rows with timezone calculations, certain optimization strategies can significantly improve query performance.

Using materialized views

For frequently-accessed timezone combinations, consider precomputing offset values:

CREATE MATERIALIZED VIEW user_events_with_offsets
ENGINE = MergeTree()
ORDER BY (user_id, event_date)
AS SELECT
    user_id,
    event_timestamp,
    timeZoneOffset(event_timestamp) AS utc_offset,
    toDate(event_timestamp) AS event_date
FROM user_events

This approach trades storage space for query speed, particularly beneficial when the same offset calculations appear in multiple queries. The materialized view updates automatically as new data arrives.

Avoiding repeated conversions

When your queries perform the same timezone calculation multiple times, consider computing it once and reusing the result:

-- Inefficient: calculates offset twice
SELECT
    event_id,
    CASE WHEN timeZoneOffset(event_time) < 0 THEN 'Western' ELSE 'Eastern' END,
    timeZoneOffset(event_time) / 3600 AS offset_hours
FROM events

-- Better: calculate once, reuse
SELECT
    event_id,
    offset_seconds,
    CASE WHEN offset_seconds < 0 THEN 'Western' ELSE 'Eastern' END,
    offset_seconds / 3600 AS offset_hours
FROM (
    SELECT event_id, timeZoneOffset(event_time) AS offset_seconds
    FROM events
)

Common mistakes and how to avoid them

Several frequent errors can cause incorrect offset calculations, leading to subtle bugs in time-sensitive applications.

Assuming implicit UTC

One of the most common mistakes involves assuming DateTime values represent UTC when they actually use the server's local timezone:

-- Dangerous: assumes UTC but might not be
SELECT timeZoneOffset(created_at) FROM users

-- Safe: explicit timezone specification
SELECT timeZoneOffset(toTimeZone(created_at, 'UTC')) FROM users

Always verify the timezone context of your DateTime columns before performing offset calculations. When in doubt, explicitly specify the timezone you're working with.

Casting to string too early

Converting timestamps to strings before offset calculation produces incorrect results because string values don't carry timezone information:

-- Wrong: string conversion loses timezone data
SELECT timeZoneOffset(toString(event_time))

-- Correct: calculate offset first, then format if needed
SELECT
    timeZoneOffset(event_time) AS offset,
    toString(event_time) AS formatted_time

Keep DateTime values in their native format until you've finished all timezone-related calculations. String formatting comes last in the processing pipeline.

Ignoring historical DST changes

DST rules change over time, and different regions have adopted or abandoned DST at various points in history. When analyzing historical data, be aware that offset calculations for the same location might differ across years:

-- Same location, different years, potentially different DST rules
SELECT
    '2005' AS year, timeZoneOffset(toDateTime('2005-06-15 12:00:00', 'America/Indiana/Indianapolis')),
    '2015' AS year, timeZoneOffset(toDateTime('2015-06-15 12:00:00', 'America/Indiana/Indianapolis'))

Indiana, for example, didn't observe DST until 2006, so summer offsets from 2005 will differ from those in 2015.

Building global IoT analytics APIs with DST-aware timezone handling

Tinybird makes it simple to build ClickHouse^®-based APIs over time-series data that span multiple timezones. Here's an example use case: creating a complete global IoT energy monitoring system that handles DST transitions and timezone-aware analytics.

Step 1: Create a data source for global IoT sensor data

First, create a data source to store IoT sensor readings from devices worldwide. Create a global_sensors.datasource file:

SCHEMA >
    `timestamp_utc` DateTime64(3, 'UTC'),
    `device_id` String,
    `location` String,
    `device_timezone` String,
    `energy_kwh` Float32,
    `temperature_c` Float32,
    `device_type` LowCardinality(String),
    `country` LowCardinality(String)

ENGINE "MergeTree"
ENGINE_PARTITION_KEY "toYYYYMM(timestamp_utc)"
ENGINE_SORTING_KEY "timestamp_utc, device_id"

Deploy the data source:

tb --cloud deploy

Ingest sample IoT sensor data using the Events API:

curl -X POST \
  -H "Authorization: Bearer $TINYBIRD_TOKEN" \
  -H "Content-Type: application/json" \
  "https://api.tinybird.co/v0/events?name=global_sensors" \
  -d '[
    {"timestamp_utc": "2024-03-10 07:00:00", "device_id": "sensor_nyc_001", "location": "New York Office", "device_timezone": "America/New_York", "energy_kwh": 12.5, "temperature_c": 22.1, "device_type": "HVAC", "country": "US"},
    {"timestamp_utc": "2024-03-10 12:00:00", "device_id": "sensor_ldn_001", "location": "London Office", "device_timezone": "Europe/London", "energy_kwh": 8.3, "temperature_c": 19.8, "device_type": "HVAC", "country": "UK"},
    {"timestamp_utc": "2024-03-10 20:00:00", "device_id": "sensor_tok_001", "location": "Tokyo Office", "device_timezone": "Asia/Tokyo", "energy_kwh": 15.2, "temperature_c": 24.5, "device_type": "HVAC", "country": "JP"}
  ]'

Step 2: Create a timezone-aware energy consumption API

Create a pipe that analyzes energy consumption patterns while handling DST transitions. Create energy_consumption_by_timezone.pipe:

TOKEN "energy_analytics_read" READ

NODE energy_analysis
SQL >
    %
    SELECT
        device_timezone,
        country,
        device_type,
        -- Convert UTC to local device time for business hour analysis
        toTimeZone(timestamp_utc, device_timezone) AS local_timestamp,
        toHour(toTimeZone(timestamp_utc, device_timezone)) AS local_hour,
        -- Calculate DST-aware UTC offset for each reading
        timeZoneOffset(toTimeZone(timestamp_utc, device_timezone)) AS utc_offset_seconds,
        timeZoneOffset(toTimeZone(timestamp_utc, device_timezone)) / 3600 AS utc_offset_hours,
        -- Determine if timestamp falls during business hours in local time
        CASE
            WHEN toHour(toTimeZone(timestamp_utc, device_timezone)) BETWEEN 9 AND 17
            THEN 'business_hours'
            ELSE 'after_hours'
        END AS time_period,
        -- Energy consumption metrics
        sum(energy_kwh) AS total_energy_kwh,
        avg(energy_kwh) AS avg_energy_kwh,
        avg(temperature_c) AS avg_temperature_c,
        count() AS readings_count
    FROM global_sensors
    WHERE timestamp_utc >= {{DateTime(start_time, '2024-03-10 00:00:00')}}
      AND timestamp_utc < {{DateTime(end_time, '2024-03-11 00:00:00')}}
    {\% if defined(timezone_filter) %}
      AND device_timezone = {{String(timezone_filter)}}
    {\% end %}
    {\% if defined(country_filter) %}
      AND country = {{String(country_filter)}}
    {\% end %}
    {\% if defined(device_type_filter) %}
      AND device_type = {{String(device_type_filter)}}
    {\% end %}
    GROUP BY
        device_timezone,
        country,
        device_type,
        local_hour,
        utc_offset_seconds,
        utc_offset_hours,
        time_period
    ORDER BY total_energy_kwh DESC
    {\% if defined(limit) %}
    LIMIT {{Int32(limit, 50)}}
    {\% end %}

TYPE ENDPOINT

Deploy the API endpoint:

tb --cloud deploy

Step 3: Query the DST-aware energy analytics API

Your timezone-aware IoT analytics API is now available. Query it for different analysis scenarios:

# Analyze energy consumption during DST transition
curl "https://api.tinybird.co/v0/pipes/energy_consumption_by_timezone.json?start_time=2024-03-10%2000:00:00&end_time=2024-03-10%2023:59:59" \
  -H "Authorization: Bearer $TINYBIRD_TOKEN"

# Compare business vs after-hours consumption by country
curl "https://api.tinybird.co/v0/pipes/energy_consumption_by_timezone.json?start_time=2024-03-10%2000:00:00&end_time=2024-03-10%2023:59:59&country_filter=US" \
  -H "Authorization: Bearer $TINYBIRD_TOKEN"

# Analyze HVAC efficiency across timezones
curl "https://api.tinybird.co/v0/pipes/energy_consumption_by_timezone.json?start_time=2024-03-10%2000:00:00&end_time=2024-03-10%2023:59:59&device_type_filter=HVAC" \
  -H "Authorization: Bearer $TINYBIRD_TOKEN"

The API returns timezone-aware analytics with accurate DST handling:

{
    "meta": [
        {"name": "device_timezone", "type": "String"},
        {"name": "country", "type": "String"},
        {"name": "device_type", "type": "String"},
        {"name": "local_timestamp", "type": "DateTime"},
        {"name": "local_hour", "type": "UInt8"},
        {"name": "utc_offset_seconds", "type": "Int32"},
        {"name": "utc_offset_hours", "type": "Float64"},
        {"name": "time_period", "type": "String"},
        {"name": "total_energy_kwh", "type": "Float32"},
        {"name": "avg_energy_kwh", "type": "Float32"},
        {"name": "avg_temperature_c", "type": "Float32"},
        {"name": "readings_count", "type": "UInt64"}
    ],
    "data": [
        {
            "device_timezone": "Asia/Tokyo",
            "country": "JP",
            "device_type": "HVAC",
            "local_timestamp": "2024-03-11 05:00:00",
            "local_hour": 5,
            "utc_offset_seconds": 32400,
            "utc_offset_hours": 9.0,
            "time_period": "after_hours",
            "total_energy_kwh": 15.2,
            "avg_energy_kwh": 15.2,
            "avg_temperature_c": 24.5,
            "readings_count": 1
        },
        {
            "device_timezone": "America/New_York",
            "country": "US",
            "device_type": "HVAC",
            "local_timestamp": "2024-03-10 03:00:00",
            "local_hour": 3,
            "utc_offset_seconds": -14400,
            "utc_offset_hours": -4.0,
            "time_period": "after_hours",
            "total_energy_kwh": 12.5,
            "avg_energy_kwh": 12.5,
            "avg_temperature_c": 22.1,
            "readings_count": 1
        }
    ],
    "rows": 2,
    "statistics": {
        "elapsed": 0.003,
        "rows_read": 3,
        "bytes_read": 256
    }
}

This approach provides:

• DST-aware timezone conversion using toTimeZone() and timeZoneOffset()
• Local business hour analysis regardless of UTC timestamp
• Accurate offset calculations during DST transitions
• Global energy consumption insights with proper timezone handling
• Flexible filtering by timezone, country, and device type

Your IoT data becomes available as a production-ready API that handles the complexity of global timezone management automatically. Start building with Tinybird's free plan to create timezone-aware analytics APIs for your IoT infrastructure.

Key takeaways and next steps

Getting UTC offset calculations right with DST support is essential for building reliable time-sensitive applications, especially when dealing with users across multiple timezones.

Recap of best practices

Keep these key points in mind when working with timeZoneOffset():

• Always specify timezone explicitly for consistent results across different server configurations
• Use IANA timezone names rather than abbreviations like EST/PST to avoid ambiguity during DST transitions
• Test with DST transition dates like March and November to verify your calculations handle edge cases correctly

The timeZoneOffset() function handles the complexity of DST rules automatically, but proper usage patterns ensure your applications remain robust across different scenarios. Remember that timezone handling affects not just display formatting but also data analysis accuracy.

Sign up for a free Tinybird plan

While ClickHouse^® provides powerful timezone functions like timeZoneOffset(), managing the underlying infrastructure can become complex as your data grows. Tinybird's managed ClickHouse^® platform significantly reduces operational overhead and provides the same timezone capabilities, so teams can focus on building features rather than managing databases. Tinybird offers a free tier suitable for testing and development. You can sign up here.

FAQs about DST-aware UTC offsets in ClickHouse^®

How do I list all supported IANA time zones in ClickHouse^®?

Query the system.time_zones table to see all available timezone identifiers:

SELECT name FROM system.time_zones WHERE name LIKE '%New_York%'

This system table contains every timezone that works with timeZoneOffset() and related functions.

Does timeZoneOffset() consider leap seconds?

No, ClickHouse^® follows POSIX time standards which do not account for leap seconds in timezone offset calculations. The function focuses on DST transitions and standard timezone rules rather than astronomical time corrections.

What happens if my server's tzdata is outdated?

ClickHouse^® relies on the system's timezone database for DST rules, so outdated tzdata can cause incorrect calculations for recent rule changes. Keep your system's timezone data current, especially if you're processing recent timestamps or need future DST predictions.

Can I index a computed offset column for faster filtering?

Yes, you can create indexes on materialized columns that store precomputed timeZoneOffset() results. This approach works well when you frequently filter or group by offset values, though it requires additional storage space for the computed values.