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TIMESTAMPTZ Field

Applications that track time across regions, such as e-commerce systems, collaboration tools, or distributed logging, need precise handling of timestamps with time zones. The TIMESTAMPTZ data type in Zilliz Cloud provides this capability by storing timestamps with their associated time zone.

What is a TIMESTAMPTZ field?​

A TIMESTAMPTZ field is a schema-defined data type (DataType.TIMESTAMPTZ) in Zilliz Cloud that processes time zone-aware input and stores all time points internally as UTC absolute time:

  • Accepted input format: TIMESTAMPTZ fields accept ISO 8601–compatible timestamp strings, including:

    • "2024-12-31 22:00:00"

    • "2024-12-31T22:00:00"

    • "2024-12-31T22:00:00+08:00"

    • "2024-12-31T22:00:00Z"

  • Timestamp parsing rules: How a timestamp is interpreted depends on whether the input string explicitly specifies a time zone:

    • If the input includes a time-zone offset (for example, +08:00 or Z), it is treated as an absolute point in time.

    • If the input does not include a time-zone offset, it is interpreted using the collection’s configured timezone. For example, if the collection timezone is Asia/Shanghai:

      • "2024-12-31 22:00:00" is interpreted as 2024-12-31T22:00:00+08:00

      • "2024-12-31T22:00:00" is interpreted as 2024-12-31T22:00:00Z, which corresponds to 2025-01-01T06:00:00+08:00

  • Internal storage: All TIMESTAMPTZ values are normalized and stored in Coordinated Universal Time (UTC).

  • Comparison and filtering: All comparison, filtering, and ordering operations on TIMESTAMPTZ fields are performed on the UTC-normalized value, ensuring consistent behavior across different time zones.

📘Notes

  • You can set nullable=True for TIMESTAMPTZ fields to allow missing values.

  • You can specify a default timestamp value using the default_value attribute in ISO 8601 format.

See Nullable & Default for details.

Basic operations​

The basic workflow of using a TIMESTAMPTZ field mirrors other scalar fields in Zilliz Cloud: define the field → insert data → query/filter.

Step 1: Define a TIMESTAMPTZ field​

To use a TIMESTAMPTZ field, explicitly define it in your collection schema when creating the collection. The following example demonstrates how to create a collection with a tsz field of type DataType.TIMESTAMPTZ.

import time
from pymilvus import MilvusClient, DataType
import datetime
import pytz

server_address = "YOUR_CLUSTER_ENDPOINT"
collection_name = "timestamptz_test123"

client = MilvusClient(uri=server_address)

if client.has_collection(collection_name):
    client.drop_collection(collection_name)

schema = client.create_schema()
# Add a primary key field
schema.add_field("id", DataType.INT64, is_primary=True)
# Add a TIMESTAMPTZ field that allows null values
schema.add_field("tsz", DataType.TIMESTAMPTZ, nullable=True)
# Add a vector field
schema.add_field("vec", DataType.FLOAT_VECTOR, dim=4)

client.create_collection(collection_name, schema=schema, consistency_level="Session")
print(f"Collection '{collection_name}' with a TimestampTz field created successfully.")

Step 2: Insert data​

Insert entities containing ISO 8601 strings with time zone offsets.

The example below inserts 8,193 rows of sample data into the collection. Each row includes:

  • a unique ID

  • a timezone-aware timestamp (Shanghai time)

  • a simple 4-dimensional vector

data_size = 10

# Get the Asia/Shanghai time zone using the pytz library
# You can use any valid IANA time zone identifier such as:
#   "Asia/Tokyo", "America/New_York", "Europe/London", "UTC", etc.
# To view all available values:
#   import pytz; print(pytz.all_timezones)
# Reference:
#   IANA database – https://www.iana.org/time-zones
#   Wikipedia – https://en.wikipedia.org/wiki/List_of_tz_database_time_zones
shanghai_tz = pytz.timezone("Asia/Shanghai")

data = [
    {
        "id": i + 1,
        "tsz": shanghai_tz.localize(
            datetime.datetime(2025, 1, 1, 0, 0, 0) + datetime.timedelta(days=i)
        ).isoformat(),
        "vec": [float(i) / 10 for i in range(4)],
    }
    for i in range(data_size)
]

client.insert(collection_name, data)
print("Data inserted successfully.")

Step 3: Filtering operations​

TIMESTAMPTZ supports scalar comparisons, interval arithmetic, and extraction of time components.

Before you can perform filtering operations on TIMESTAMPTZ fields, make sure:

  • You have created an index on each vector field.

  • The collection is loaded into memory.

Show example code
# Create index on vector field
index_params = client.prepare_index_params()
index_params.add_index(
    field_name="vec",
    index_type="AUTOINDEX",
    index_name="vec_index",
    metric_type="COSINE"
)
client.create_index(collection_name, index_params)
print("Index created successfully.")

# Load the collection
client.load_collection(collection_name)
print(f"Collection '{collection_name}' loaded successfully.")

Query with timestamp filtering​

Use arithmetic operators like ==, !=, <, >, <=, >=. For a full list of arithmetic operators available in Zilliz Cloud, refer to Arithmetic Operators.

📘Notes

Chained range expressions (for example, lower_bound < tsz < upper_bound) are not supported.

Use logical conjunction instead: tsz > lower_bound AND tsz < upper_bound.

The example below filters entities with timestamps (tsz) that are not equal to 2025-01-03T00:00:00+08:00:

# Query for entities where tsz is not equal to '2025-01-03T00:00:00+08:00'
expr = "tsz != ISO '2025-01-03T00:00:00+08:00'"

results = client.query(
    collection_name=collection_name,
    filter=expr,
    output_fields=["id", "tsz"],
    limit=10
)

print("Query result: ", results)

# Expected output:
# Query result:  data: ["{'id': 1, 'tsz': '2024-12-31T16:00:00Z'}", "{'id': 2, 'tsz': '2025-01-01T16:00:00Z'}", "{'id': 4, 'tsz': '2025-01-03T16:00:00Z'}", "{'id': 5, 'tsz': '2025-01-04T16:00:00Z'}", "{'id': 6, 'tsz': '2025-01-05T16:00:00Z'}", "{'id': 7, 'tsz': '2025-01-06T16:00:00Z'}", "{'id': 8, 'tsz': '2025-01-07T16:00:00Z'}", "{'id': 9, 'tsz': '2025-01-08T16:00:00Z'}", "{'id': 10, 'tsz': '2025-01-09T16:00:00Z'}", "{'id': 11, 'tsz': '2025-01-10T16:00:00Z'}"]

In the example above,

  • tsz is the TIMESTAMPTZ field name defined in the schema.

  • ISO '2025-01-03T00:00:00+08:00' is a timestamp literal in ISO 8601 format, including its time-zone offset.

  • != compares the field value against that literal. Other supported operators include ==, <, <=, >, and >=.

Interval operations​

You can perform arithmetic on TIMESTAMPTZ fields using INTERVAL values in the ISO 8601 duration format. This allows you to add or subtract durations, such as days, hours, or minutes, from a timestamp when filtering data.

For example, the following query filters entities where the timestamp (tsz) plus zero days is not equal to 2025-01-03T00:00:00+08:00:

expr = "tsz + INTERVAL 'P0D' != ISO '2025-01-03T00:00:00+08:00'"

results = client.query(
    collection_name, 
    filter=expr, 
    output_fields=["id", "tsz"], 
    limit=10
)

print("Query result: ", results)

# Expected output:
# Query result:  data: ["{'id': 1, 'tsz': '2024-12-31T16:00:00Z'}", "{'id': 2, 'tsz': '2025-01-01T16:00:00Z'}", "{'id': 4, 'tsz': '2025-01-03T16:00:00Z'}", "{'id': 5, 'tsz': '2025-01-04T16:00:00Z'}", "{'id': 6, 'tsz': '2025-01-05T16:00:00Z'}", "{'id': 7, 'tsz': '2025-01-06T16:00:00Z'}", "{'id': 8, 'tsz': '2025-01-07T16:00:00Z'}", "{'id': 9, 'tsz': '2025-01-08T16:00:00Z'}", "{'id': 10, 'tsz': '2025-01-09T16:00:00Z'}", "{'id': 11, 'tsz': '2025-01-10T16:00:00Z'}"]
📘Notes

INTERVAL values follow the ISO 8601 duration syntax. For example:

  • P1D → 1 day

  • PT3H → 3 hours

  • P2DT6H → 2 days and 6 hours

You can use INTERVAL arithmetic directly in filter expressions, such as:

  • tsz + INTERVAL 'P3D' → Adds 3 days

  • tsz - INTERVAL 'PT2H' → Subtracts 2 hours

Search with timestamp filtering​

You can combine TIMESTAMPTZ filtering with vector similarity search to narrow results by both time and similarity.

# Define a time-based filter expression
filter = "tsz > ISO '2025-01-05T00:00:00+08:00'"

res = client.search(
    collection_name=collection_name,             # Collection name
    data=[[0.1, 0.2, 0.3, 0.4]],                  # Query vector (must match collection's vector dim)
    limit=5,                                      # Max. number of results to return
    filter=filter,                                # Filter expression using TIMESTAMPTZ
    output_fields=["id", "tsz"],  # Fields to include in the search results
)

print("Search result: ", res)

# Expected output:
# Search result:  data: [[{'id': 10, 'distance': 0.9759000539779663, 'entity': {'tsz': '2025-01-09T16:00:00Z', 'id': 10}}, {'id': 9, 'distance': 0.9759000539779663, 'entity': {'tsz': '2025-01-08T16:00:00Z', 'id': 9}}, {'id': 8, 'distance': 0.9759000539779663, 'entity': {'tsz': '2025-01-07T16:00:00Z', 'id': 8}}, {'id': 7, 'distance': 0.9759000539779663, 'entity': {'tsz': '2025-01-06T16:00:00Z', 'id': 7}}, {'id': 6, 'distance': 0.9759000539779663, 'entity': {'tsz': '2025-01-05T16:00:00Z', 'id': 6}}]]
📘Notes

If your collection has two or more vector fields, you can perform hybrid search operations with timestamp filtering. For details, refer to Multi-Vector Hybrid Search.

Advanced usage​

For advanced usage, you can manage time zones at different levels (e.g. database, collection, or query) or accelerate queries on TIMESTAMPTZ fields using indexes.

Manage time zones at different levels​

You can control the time zone for TIMESTAMPTZ fields at the collection or query/search level.

Level

Parameter

Scope

Priority

Collection

timezone

Overrides the database default time zone setting for that collection

Medium

Query/search/hybrid search

timezone

Temporary overrides for one specific operation

Highest

For step-by-step instructions and code samples, refer to the dedicated pages:

Accelerate queries​

By default, queries on TIMESTAMPTZ fields without an index will perform a full scan of all rows, which can be slow on large datasets. To accelerate timestamp queries, create an AUTOINDEX index on your TIMESTAMPTZ field.

For details, refer to Index Scalar Fields.