Silent pg_restore Bug Can Lose Entire Tables When Restoring Supabase Backups

·9 views

A developer discovered that restoring a Supabase database backup using standard pg_restore can silently drop entire tables without raising a fatal error. The issue occurs because Supabase-specific functions like auth.uid(), commonly used as column defaults, fail when restored into a plain PostgreSQL environment that lacks the auth schema. By default, pg_restore logs these errors but continues execution, leaving the affected tables and their data absent from the restored database. The bug is particularly dangerous because developers testing Supabase backups are already conditioned to ignore auth-related errors, making missing tables easy to overlook. The fix involves creating stub roles and a minimal auth schema in the target database before running pg_restore, ensuring dependent objects resolve correctly during restoration.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

WClickHouse Library Loads 100,000-Row Pandas DataFrames into ClickHouse in Under 0.6s

A Python library called WClickHouse has introduced an insert_dataframe() method designed to load Pandas DataFrames into ClickHouse analytical databases at near-network speed, eliminating the need for slow row-by-row iteration. The tool addresses common pain points for data scientists, including type incompatibilities between Pandas float64/NaN values and ClickHouse's nullable types, as well as Jupyter Notebook crashes when exporting large datasets. According to the developers, the method can ingest 100,000 rows in under 0.6 seconds using a vectorized backend built on Apache Arrow and Pydantic v2. The library supports Python versions 3.9 through 3.14 and claims over 95% test coverage against real ClickHouse instances. WClickHouse is available on both GitHub and PyPI as an open-source project.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

WClickHouse Library Inserts 100K Pandas Rows into ClickHouse in Under 0.6 Seconds

An open-source Python library called WClickHouse offers a method called insert_dataframe() that loads Pandas DataFrames directly into ClickHouse databases using a vectorized backend. The tool addresses common pain points for data scientists, including slow row-by-row insertion methods like df.iterrows() that can take up to 20 minutes and type mismatches between Pandas and ClickHouse data types. The library automatically aligns Pandas dtypes with ClickHouse table schemas, eliminating manual conversion steps. It supports Python versions 3.9 through 3.14 and is built on Apache Arrow and Pydantic v2, with over 95% test coverage verified against live ClickHouse servers. WClickHouse is available on both GitHub and PyPI as part of an ongoing open-source engineering series.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

wFabricSecurity Brings Cryptographic Microsegmentation to Blockchain Nodes

An open-source tool called wFabricSecurity introduces cryptographic microsegmentation for Hyperledger Fabric blockchain networks, enforcing explicit communication permissions between nodes. The library applies a default-deny policy, meaning all inter-node communication is blocked unless specifically authorized. Permissions are managed through a Common Name (CN) based matrix, supporting both unidirectional and bidirectional communication rules. The tool is designed to prevent threats such as unauthorized lateral movement and compromised worker nodes issuing malicious commands to orchestrators. Compatible with Python 3.10 and above, it also includes code integrity hashing and token-bucket rate limiting.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

wFabricSecurity enforces cryptographic micro-segmentation for blockchain nodes

An open-source library called wFabricSecurity introduces cryptographic micro-segmentation to control communication between blockchain nodes in Hyperledger Fabric environments. The tool operates on a default-deny policy, meaning all inter-node communication is blocked unless explicitly permitted by the administrator. Permissions are registered using cryptographic Common Names, and the system supports both unidirectional and bidirectional communication rules. This approach addresses risks such as unauthorized lateral movement and compromised nodes sending malicious commands to orchestrators. The library is compatible with Python 3.10 and above, and is available on both GitHub and PyPI.

0 comments Read more at DEV Community