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文章背景与核心概要

本文介绍了 Supabase 正在私测阶段推出的全新功能——Supabase 分析存储桶（Analytics Buckets）。该解决方案专为大规模分析而优化，并内置了对 Apache Iceberg 开放表格式的支持。它通过直接集成到 Supabase Studio 中，并借助全新的 Supabase Iceberg Wrapper，让开发者能够通过标准 SQL 查询分析数据，同时确保完全的数据可移植性和对开放标准的合规性。

文章详细阐述了 Supabase 选择 Iceberg 的原因（如无底数据模型、快照与版本控制、架构演进及开放标准）、分析存储桶的架构区别、创建命名空间与表的具体代码示例、如何通过 SQL 查询分析数据，以及目前的 Alpha 阶段限制和未来路线图。

Supabase Analytics Buckets with Iceberg Support

Summary

Supabase is launching Supabase Analytics Buckets in private alpha—a new storage solution optimized for large-scale analytics with built-in support for the Apache Iceberg open table format. Integrated directly into Supabase Studio and powered by the new Supabase Iceberg Wrapper, these buckets allow you to query analytical data via standard SQL while ensuring full data portability and open standards compliance.

Supabase 正在私测阶段推出 Supabase Analytics Buckets——这是一种针对大规模分析进行优化的全新存储解决方案，内置了对 Apache Iceberg 开放表格式的支持。通过直接集成到 Supabase Studio 并由全新的 Supabase Iceberg Wrapper 提供支持，这些存储桶允许您通过标准 SQL 查询分析数据，同时确保完全的数据可移植性和对开放标准的合规性。

Why Iceberg?

Apache Iceberg is a high-performance, open table format for large-scale analytics on object storage. It brings database-like features to the flexibility of flat files.

Supabase chose Iceberg for several key reasons: * Bottomless Data Model: Append-only and immutable history. * Snapshotting & Versioning: Built-in time travel capabilities. * Schema Evolution: Seamless adaptation to changing data structures. * Open Standards: Prevents vendor lock-in, aligning with Supabase's commitment to open standards and portability.

为什么选择 Iceberg？

Apache Iceberg 是一种高性能的开放表格式，用于在对象存储上进行大规模分析。它将类似数据库的功能带给平面文件的灵活性。

Supabase 选择 Iceberg 的几个关键原因包括： * 无底数据模型（Bottomless Data Model）： 仅追加（Append-only）且不可变的歷史记录。 * 快照与版本控制（Snapshotting & Versioning）： 内置时间旅行（Time Travel）功能。 * 架构演进（Schema Evolution）： 无缝适应不断变化的数据结构。 * 开放标准（Open Standards）： 防止供应商锁定，这符合 Supabase 对开放标准和可移植性的承诺。

Setting up Analytics Buckets

Once your project is accepted into the private alpha, you can create an Analytics Bucket via Supabase Studio by navigating to Storage > New bucket.

Key Differences & Storage Architecture

  • Analytics buckets are entirely separate from standard Supabase Storage buckets; file types cannot be mixed between the two.
  • They are stored in a new system table: storage.buckets_iceberg (isolated from storage.buckets and storage.objects).
  • The listBuckets() endpoint returns a merged list of both standard and analytics buckets for UI and API consistency.

Creating an Iceberg Namespace and Table

After creating the bucket, copy the WAREHOUSE, VAULT_TOKEN, and CATALOG_URI connection details. You can then use tools like pyiceberg to create your namespaces and tables:

import datetime
import pyarrow as pa
from pyiceberg.catalog.rest import RestCatalog
from pyiceberg.exceptions import NamespaceAlreadyExistsError, TableAlreadyExistsError

# Define catalog connection details (replace variables)
WAREHOUSE = ...
VAULT_TOKEN = ...
CATALOG_URI = ...

# Connect to Supabase Data Catalog
catalog = RestCatalog(
    name="catalog",
    warehouse=WAREHOUSE,
    uri=CATALOG_URI,
    token=VAULT_TOKEN,
)

# Schema and Table Names
namespace_name = "market"
table_name = "prices"

# Create default namespace
catalog.create_namespace(namespace_name)

df = pa.table({
    "tenant_id": pa.array([], type=pa.string()),
    "store_id": pa.array([], type=pa.string()),
    "item_id": pa.array([], type=pa.string()),
    "price": pa.array([], type=pa.float64()),
    "timestamp": pa.array([], type=pa.int64()),
})

# Create an Iceberg table
table = catalog.create_table(
    (namespace_name, table_name),
    schema=df.schema,
)

Back in Supabase Studio, you will see your newly created namespace. Click Connect and select a Target Schema (it is recommended to use a standalone schema rather than public to prevent exposing your analytical tables over the project's REST API).

设置分析存储桶

一旦您的项目通过了私测申请，您就可以通过 Supabase Studio 导航至 Storage > New bucket 来创建分析存储桶。

关键区别与存储架构

  • 分析存储桶与标准的 Supabase 存储桶完全隔离；两者之间不能混用文件类型。
  • 它们存储在新的系统表中：storage.buckets_iceberg（与 storage.buckets 和 storage.objects 隔离）。
  • listBuckets() 端点将返回标准存储桶和分析存储桶的合并列表，以保持 UI 和 API 的一致性。

创建 Iceberg 命名空间与表

创建存储桶后，复制 WAREHOUSE、VAULT_TOKEN 和 CATALOG_URI 连接详细信息。然后，您可以使用诸如 pyiceberg 的工具来创建命名空间和表：

import datetime
import pyarrow as pa
from pyiceberg.catalog.rest import RestCatalog
from pyiceberg.exceptions import NamespaceAlreadyExistsError, TableAlreadyExistsError

# Define catalog connection details (replace variables)
WAREHOUSE = ...
VAULT_TOKEN = ...
CATALOG_URI = ...

# Connect to Supabase Data Catalog
catalog = RestCatalog(
    name="catalog",
    warehouse=WAREHOUSE,
    uri=CATALOG_URI,
    token=VAULT_TOKEN,
)

# Schema and Table Names
namespace_name = "market"
table_name = "prices"

# Create default namespace
catalog.create_namespace(namespace_name)

df = pa.table({
    "tenant_id": pa.array([], type=pa.string()),
    "store_id": pa.array([], type=pa.string()),
    "item_id": pa.array([], type=pa.string()),
    "price": pa.array([], type=pa.float64()),
    "timestamp": pa.array([], type=pa.int64()),
})

# Create an Iceberg table
table = catalog.create_table(
    (namespace_name, table_name),
    schema=df.schema,
)

回到 Supabase Studio 中，您将看到刚刚创建的命名空间。点击 Connect（连接） 并选择一个 Target Schema（目标 Schema）（建议使用独立的 Schema 而不是 public，以防止通过项目的 REST API 暴露您的分析表）。

Querying Analytics Buckets

Viewing an analytics bucket in Supabase Studio redirects you to the Table Editor, where a table explorer powered by the Supabase Iceberg Wrapper displays your data instead of raw Parquet files.

You can query your analytical data directly using SQL:

select
  *
from market_analytics.prices;

查询分析存储桶

在 Supabase Studio 中查看分析存储桶会将您重定向到表编辑器（Table Editor），在这里，由 Supabase Iceberg Wrapper 驱动的表浏览器将显示您的数据，而不是原始的 Parquet 文件。

您可以直接使用 SQL 查询您的分析数据：

select
  *
from market_analytics.prices;

Writing to Analytics Buckets

Writing data natively is currently a work in progress. For now, you can populate Analytics Buckets using your own ingestion pipelines or Iceberg-compatible tooling.

Native write capabilities will be added to the Supabase Iceberg Wrapper as soon as write support lands in the upstream iceberg-rust client library, completing a seamless write → store → query workflow entirely inside Supabase.

写入分析存储桶

原生写入数据目前正在开发中。目前，您可以使用自己的摄取管道或与 Iceberg 兼容的工具来填充分析存储桶。

一旦上游的 iceberg-rust 客户端库 落地了写入支持，原生写入功能将立即添加到 Supabase Iceberg Wrapper 中，从而在 Supabase 内部完整实现无缝的 写入 → 存储 → 查询 工作流。

Alpha Launch Limits

During the private alpha, Analytics Buckets are subject to the following constraints: * Two analytics buckets per project * Up to five namespaces per bucket * Ten tables per namespace * Pricing details will be announced in the coming weeks * Standard objects cannot be stored in analytics buckets

Alpha 发布限制

在私测（Alpha）阶段，分析存储桶受以下限制： * 每个项目两个分析存储桶 * 每个存储桶最多五个命名空间 * 每个命名空间最多十张表 * 定价详情将在未来几周内公布 * 标准对象不能存储在分析存储桶中

Roadmap and What's Next

This launch is the first step toward robust analytical capabilities in Supabase. Upcoming milestones include: * SQL Catalog Support: Explore Iceberg table metadata directly from the database. * Deeper Studio Integration: Enhanced schema inspection, column-level filtering, and time-travel query tools. * HTAP Backend Realization: A fully unified environment to write, store, and query analytical data seamlessly.

路线图与下一步计划

此次发布是 Supabase 迈向强大分析能力的第一步。接下来的里程碑包括： * SQL Catalog 支持： 直接从数据库中探索 Iceberg 表元数据。 * 更深度的 Studio 集成： 增强的 Schema 检查、列级过滤和时间旅行查询工具。 * HTAP 后端实现： 一个完全统一的环境，用于无缝地写入、存储和查询分析数据。

Try It Out

Join the waitlist here to secure early access and start working with bottomless, time-travel-capable analytics data inside Supabase.

立即试用

在此处加入候补名单，以获得早期访问权限，并开始在 Supabase 内部使用无底的、具备时间旅行能力的分析数据。