Shinro for ClickHouse Migration

How it works.

A migration runs in six phases. Shinro automates the deterministic work across the first four. Cutover and optimization stay with engineers, because they are decisions rather than steps. The platform operates through a web-based conversational interface with companion command-line utilities for scripting and pipeline integration.

Assessment and analysis begins with Shinro Profiler inventorying the source system: data volumes, query patterns and frequency, version and dialect incompatibilities, and application dependencies. Shinro Code Analyzer statically analyses queries, UDFs and deprecated feature usage in parallel, producing a translation backlog scored by effort and risk. Deployment architecture, HA and DR posture, network topology and cost are assessed alongside, so the shape of the work is known before any of it starts.

Design and provisioning is where Shinro DDL Generator produces the target schema from the profile and code analysis data: table engines, initial sort keys, partitioning and TTL. Engineers provision the cluster in parallel, covering capacity planning, replication, multi-region failover, backups and SSO. The synchronisation mechanism is decided per table, reconciliation thresholds are set, and both the cutover and rollback runbooks are written before any data moves.

Conversion and verification deploys the DDL, applies the translated queries and application code, and migrates a small representative data set into a lower environment. Shinro Validator proves functional parity against the frequent-query catalogue and surfaces the incompatibilities that only appear against real data. The phase closes when the translation backlog reaches zero.

Migration and verification is where Shinro Migrator backfills historical data in bulk, establishes real-time change data capture, and backfills materialized views. Dual-write goes on so both systems receive every write, and Shinro Validator runs continuous reconciliation alongside drift and replication-lag monitoring, load and failover testing at full scale. By the end of this phase the target is in shadow: fully current, serving no user reads.

Cutover is engineer-led. Reads shift in stages, canary then split read then full, with query metrics, application performance and cluster load validated at each step. A fallback window is held in which dual-write remains active and the source stays warm, so rollback costs minutes rather than a restore. Retiring dual-write and disabling the old write paths is the point of no return, and it is a deliberate, recorded decision.

Optimization and operation is engineer-led too. Sort keys, materialized view chains, partitioning and TTL are tuned against observed production query patterns, memory and storage are optimised, and the application is refactored onto newer platform features. Steady-state operations follow: cluster metrics, alerting, access policies, SLA verification, auto-scaling and a capacity planning cadence.