/cloudflare-deploy

API Reference

R2 Data Catalog exposes standard Apache Iceberg REST Catalog API.

Quick Reference

Most common operations:

REST Endpoints

Base: https://<account-id>.r2.cloudflarestorage.com/iceberg/<bucket-name>

Authentication: Bearer token in header: Authorization: Bearer <token>

PyIceberg Client API

Most users use PyIceberg, not raw REST.

Connection

from pyiceberg.catalog.rest import RestCatalog

catalog = RestCatalog(
    name="my_catalog",
    warehouse="<bucket-name>",
    uri="<catalog-uri>",
    token="<api-token>",
)

Namespace Operations

from pyiceberg.exceptions import NamespaceAlreadyExistsError

namespaces = catalog.list_namespaces()  # [('default',), ('logs',)]
catalog.create_namespace("logs", properties={"owner": "team"})
catalog.drop_namespace("logs")  # Must be empty

Table Operations

from pyiceberg.schema import Schema
from pyiceberg.types import NestedField, StringType, IntegerType

schema = Schema(
    NestedField(1, "id", IntegerType(), required=True),
    NestedField(2, "name", StringType(), required=False),
)
table = catalog.create_table(("logs", "app_logs"), schema=schema)
tables = catalog.list_tables("logs")
table = catalog.load_table(("logs", "app_logs"))
catalog.rename_table(("logs", "old"), ("logs", "new"))

Data Operations

import pyarrow as pa

data = pa.table({"id": [1, 2], "name": ["Alice", "Bob"]})
table.append(data)
table.overwrite(data)

# Read with filters
scan = table.scan(row_filter="id > 100", selected_fields=["id", "name"])
df = scan.to_pandas()

Schema Evolution

from pyiceberg.types import IntegerType, LongType

with table.update_schema() as update:
    update.add_column("user_id", IntegerType(), doc="User ID")
    update.rename_column("msg", "message")
    update.delete_column("old_field")
    update.update_column("id", field_type=LongType())  # int→long only

Time-Travel

from datetime import datetime, timedelta

# Query specific snapshot or timestamp
scan = table.scan(snapshot_id=table.snapshots()[-2].snapshot_id)
yesterday_ms = int((datetime.now() - timedelta(days=1)).timestamp() * 1000)
scan = table.scan(as_of_timestamp=yesterday_ms)

Partitioning

from pyiceberg.partitioning import PartitionSpec, PartitionField
from pyiceberg.transforms import DayTransform
from pyiceberg.types import TimestampType

partition_spec = PartitionSpec(
    PartitionField(source_id=1, field_id=1000, transform=DayTransform(), name="day")
)
table = catalog.create_table(("events", "actions"), schema=schema, partition_spec=partition_spec)
scan = table.scan(row_filter="day = '2026-01-27'")  # Prunes partitions

Table Maintenance

Compaction

files = table.scan().plan_files()
avg_mb = sum(f.file_size_in_bytes for f in files) / len(files) / (1024**2)
print(f"Files: {len(files)}, Avg: {avg_mb:.1f} MB")

table.rewrite_data_files(target_file_size_bytes=128 * 1024 * 1024)

When: Avg <10MB or >1000 files. Frequency: High-write daily, medium weekly.

Snapshot Expiration

from datetime import datetime, timedelta

seven_days_ms = int((datetime.now() - timedelta(days=7)).timestamp() * 1000)
table.expire_snapshots(older_than=seven_days_ms, retain_last=10)

Retention: Production 7-30d, dev 1-7d, audit 90+d.

Orphan Cleanup

three_days_ms = int((datetime.now() - timedelta(days=3)).timestamp() * 1000)
table.delete_orphan_files(older_than=three_days_ms)

⚠️ Always expire snapshots first, use 3+ day threshold, run during low traffic.

Full Maintenance

# Compact → Expire → Cleanup (in order)
if len(table.scan().plan_files()) > 1000:
    table.rewrite_data_files(target_file_size_bytes=128 * 1024 * 1024)
seven_days_ms = int((datetime.now() - timedelta(days=7)).timestamp() * 1000)
table.expire_snapshots(older_than=seven_days_ms, retain_last=10)
three_days_ms = int((datetime.now() - timedelta(days=3)).timestamp() * 1000)
table.delete_orphan_files(older_than=three_days_ms)

Metadata Inspection

table = catalog.load_table(("logs", "app_logs"))
print(table.schema())
print(table.current_snapshot())
print(table.properties)
print(f"Files: {len(table.scan().plan_files())}")

Error Codes

See gotchas.md for detailed troubleshooting.