Recomendación: Use Google Cloud Storage as the external storage for ClickHouse and start reading data directly from GCS while writing new data to the bucket. This approach minimizes local I/O, simplifies backups, and makes batch analytics repeatable. Once you started the integration, you will gain more predictable I/O and faster reload of historical data.
Step 1: in Google Cloud Console, create a service account, grant the minimal roles (for example Storage Object Admin for upload and delete) and download the сгенерированного JSON key. Place the key on the ClickHouse server and restrict access via the настроек. Step 2: in ClickHouse config, specify cloud_storage with provider=gcs, bucket=my-bucket, location=US-CENTRAL1, and credentials_file=/etc/clickhouse/creds.json. If credentials were previously configured, rotate to this new key and keep the файл в секрете.
Operational guidance: to achieve reliable throughput, use пакетные upload, and organize data by каждой table under separate prefixes. Specify a parameter like upload_concurrency and a retention policy that includes delete of old objects. Start with двух regional servers and a single location to validate consistency, then extend to a second location as needed. Next, monitor with GCS logs and ClickHouse metrics to catch latency spikes early.
Security and licensing: restrict network access to trusted IPs, enable encryption at rest and in transit, and store credentials in a secure vault. The solution relies on licensed components of ClickHouse and Google Cloud, so ensure your licensing is in order before enabling it in production. If you see common issues, such as permission denials or missing objects, verify the bucket name, location, and credentials_file, including lifecycle rules and access control lists.
Disk Creation for Google Cloud Storage and ClickHouse Integration
Recommendation: Create a dedicated GCS-backed disk named gcs_disk_01 and mount bucket_02 via fuse; use a service account from your project with access to bucket_02 under account settings. Disk created for this развертывания will support stable reads and writes of файлы and track удаления events during test deployments for this integration.
- Prepare GCS and IAM
- Verify that бакетов bucket_02 exists in your project and grant a service account access to storage objects (roles/storage.objectAdmin or equivalent). Store credentials securely and reference them from connectors in your deployment.
- Document the account and project context to keep ownership clear for auditing and rotations.
- Define a ClickHouse Disk backed by GCS
- In clickhouse-server/config.d/disk_gcs.xml, create a disk named gcs_disk with type s3 and endpoint storage.googleapis.com, bucket bucket_02, and a path under /каталог. Attach the credentials from the service account and mark the disk as supported for data blocks used by samples.
- Choose an engine-appropriate layout, for example using a path that aligns with your table locations where data resides.
- Mount with fuse
- Mount the GCS bucket to a local directory, for example /mnt/gcs/bucket_02, using fuse (fuse) or gcsfuse. Enable allow_other if multiple users or connectors access the mounted directory, and monitor latency across pages and reads.
- Create tables and select engine
- Use engine ReplicatedMergeTree for fault tolerance: CREATE TABLE samples_table (id UInt64, value String) ENGINE = ReplicatedMergeTree('/clickhouse/tables/{shard}/samples_table', '{replica}') ORDER BY id; this demonstrates create actions and supports a replica_2 configuration for testing under multiple nodes.
- When you need transactional semantics, consider additional engines or settings, but keep this pattern for базовых задач и тестов.
- Data lifecycle, stages, and conditional tests
- Stages: 1) initial load into the disk, 2) replication to nodes, 3) archival to бакетов. Use conditional checks to run heavy operations only if the outer environment is configured for this etiology.
- Define conditional workflows for эта установка, so that test pages do not overwrite production data.
- Validation and maintenance
- Insert sample values into the table and verify that данные flow correctly. Check that удаления appear in audit logs, and confirm that каталоги under the mounted path contain expected файлы objects.
- Ensure connectors on multiple page requests can read from and write to bucket_02, and monitor node load and storage usage across pages and их replicas, including replica_2, to validate smooth operation.
- Confirm that the deployment supports this configuration on all targeted nodes and that the project’s access policy remains aligned with ongoing operations.
Prerequisites for Disk Creation in Google Cloud Storage for ClickHouse
Create a dedicated Google Cloud Storage bucket and a service account before you create any disk for ClickHouse. This конфигурационный setup must be present on all servers, including chnode1, and включает granting the roles/storage.objectAdmin permission so ClickHouse can write and read objects. Generate credentials.json from the service account and store it securely; reference it through the objectsource path in the ClickHouse configuration. The bucket layout must include a folder named namefolder for disk data, and write access must cover that folder. This approach применим to двух CH nodes and replica_1, ensuring full visibility of the informationsamples and metadata.
Enable the Google Cloud Storage API and create a service account with roles/storage.objectAdmin. Ensure every CH node is connected to the bucket and can access data through the credentials.json referenced by objectsource in ClickHouse. Validate copyobject operations between prefixes and confirm that object metadata is populated correctly (size, lastModified). This setup provides access for серверов and guarantees consistent behavior across the cluster.
Define the bucket layout to support stable disk paths: include a root folder namefolder and per-disk prefixes such as replica_1 and replica_2. Use reference to these paths in the ClickHouse Disk configuration, and set objectsource to map to gs://bucket/namefolder/{replica_name}. The metadata fields (size, lastModified, contentType) must be present on every object and surfaced in informationsamples for audits and recovery checks.
Configure two disks on two replicas: map дDISке to gs://bucket/namefolder/replica_1 and gs://bucket/namefolder/replica_2, ensuring only these prefixes are used for disk I/O. In the CH config, declare disks with type google_cloud_storage and paths that point to the corresponding prefixes, e.g., path: gs://bucket/namefolder/replica_1. Ensure the credentials.json is loaded via objectsource and referenced through reference in the configuration. This setup must be accessible under the servers section, including chnode1, to provide full availability and seamless failover.
Test readiness steps: perform a write to replica_1 using write, then copyobject to replica_2 to validate cross-prefix replication. Verify that the object metadata is replicated and that access controls allow read and write from all сервеов. Run a quick informationsamples sweep to confirm that objectsource mappings resolve correctly and that full path references resolve without errors before promoting disk usage to production.
Create a GCS Bucket with Proper Naming and Lifecycle Rules
Choose a descriptive, scalable naming convention for the бакет and apply a region-aware structure. Use the pattern project-env-регионе-products, which makes it easy to filter by owner and product line in the storage UI and APIs. This approach helps manage files (файлы) and binary assets across teams and environments. If you previously used flat names, switch to this convention for better organization and future criatividade in project создания and management.
Configure lifecycle rules to prune outdated objects and optimize storage costs. Set the default (умолчанию) to retain only what you need locally, then delete or move long-tail data. Enable a rule that deletes objects after 90 days, and another that transitions older items to Nearline or Coldline storage when appropriate. Ensure the rule affects only enabled objects, and adjust the policy label accordingly. Implement these controls on the server side to support automated perform actions and maintain a successful policy state.
Control access with signedurl for time-bound sharing. For internal workflows, create a user with limited IAM permissions, then generate signed URLs to share specific objects. If you need both internal and external access, configure storage_allowed_locations to both regional and multi-regional options. There, monitor access events to adjust policies as needed. There are monitoring logs for signedurl usage to verify results and maintain security, then you can respond quickly if an access attempt appears unexpected.
| Aspect | Recomendación | Example |
|---|---|---|
| Naming | Use a structured pattern including project, env, region, and domain | myproj-prod-регионе-products |
| Ciclo de vida | Enable 90-day delete rule; move older data to cheaper storage | Objects older than 90 days: delete; older than 180 days: Coldline |
| Access | Use signedurl for time-bound sharing | Signed URL valid for 24 hours |
| Location policy | Set storage_allowed_locations to both regional and multi-regional | storage_allowed_locations: both |
For templates and samples, informationsamples provide concrete patterns you can adapt to your projects, ensuring the rules cover бакет creation, objects handling, and user-facing access controls.
Assign IAM Roles and Service Accounts for ClickHouse Access
Recomendación: Create a dedicated service account for ClickHouse and bind it to bucket_name with the minimum roles to perform the required operations. This keeps хранение isolated and reduces exposure across the project, while enabling predictable access for your ClickHouse workload.
In your GCP project, name the SA clickhouse-storage-sa and add a description like "ClickHouse access to GCS." Record the created datetime and reference this entry in your security policy. The same SA will be used by your ClickHouse deployment, and you can reuse its identity across components for a cohesive conectado flow.
In this section, configure access on bucket_name with a least-privilege approach. Grant on the bucket the following roles: roles/storage.objectViewer for descargar and read operations, and roles/storage.objectCreator to permit object moveobject and uploads. If you must overwrite or delete objects, add roles/storage.objectAdmin but keep the scope tight to this bucket to maintain a same level of control and minimize risk. This specific combination supports both reading data and writing new objects with enabled security boundaries.
To avoid static keys, bind the ClickHouse workload using Workload Identity Federation. Map the Kubernetes service account used by ClickHouse to the GCP SA so credentials stay conectado without distributing secrets. After mapping, verify that the ClickHouse process performs data fetches against bucket_name using the bound identity, and confirm the path aligns with your definition of access control.
Include a policy clause that restricts the SA to bucket_name and to a defined path within that bucket. Use a dedicated clause to prevent cross-bucket access and ensure the policy is referenced by your deployment. This keeps the access boundary tight and makes auditing straightforward for your team and for gebruikers auditing workflows.
Enable Cloud Audit Logs for storage.googleapis.com and monitor access events. Tie each event to the description and name of the service account so you can quickly identify actions that were performed by ClickHouse. The logs will show who accessed bucket_name, what operation occurred (download, moveobject, or delete), and the fecha y hora of the activity. This provides a clear trail for compliance and incident response, especially when будет detected unusual access.
Test the setup by performing a targeted sequence: authenticate with the ClickHouse SA, execute a descargar from bucket_name, then run a moveobject operation within the same bucket, and finally verify that the object exists with the expected metadata. Confirm that the actions appear in the audit trail under the same service account and that the permissions reflect the full lifecycle of the objects involved. Use these tests to validate that the section requirements are met and that your your policies hold under real load.
If you need to grant access to пользователей in your organization, attach the same IAM roles to a group-bound service account or create a second SA scoped to a separate bucket with the same description conventions. Keep the name and created fields aligned so the policy remains referenced across teams. This approach ensures controlled access while avoiding duplicate credentials and maintaining a clean, auditable history of changes.
This strategy results in a practical, secure model for Google Cloud Storage and ClickHouse integration, with clear boundaries, auditable events, and a repeatable process you can reuse for other buckets or workloads in your environment. Use this section as the blueprint for future deployments and updates to your IAM configuration.
Configure ClickHouse to Use GCS as a Disk via S3-Compatible Endpoint
Recommendation: Create a dedicated S3-compatible disk in ClickHouse that targets Google Cloud Storage through the endpoint https://storage.googleapis.com. This reference setup ensures data access через GCS and координируется across the cluster, so каждого узла can read and write the same data. Use a single account for management and создайте separate credentials for users you want to grant access, then bind them to the S3 endpoint.
Folder layout: in the bucket, create a top-level folder like clickhouse_data and inside it per-replica folders such as replica_2. The path there would be clickhouse_data/replica_2. For each server_id, store its данные under its folder. This approach provides double redundancy and makes restoration straightforward. These folders are referenced by the storage policy and support consistent operations for each replica.
Credentials: In Google Cloud, создайте S3-compatible service account and generate HMAC keys. Keep account secrets secure; in ClickHouse, store the keys in the credentials store and reference them in the disk config. Want to limit access to the bucket and a specific prefix (path) for users who upload data, then assign read/write rights on the account. Use the account's access_key_id and secret_access_key so the integration remains auditable and easy to rotate.
Config: In ClickHouse, define a disk named gcs_s3 with type s3, endpoint https://storage.googleapis.com, bucket your_bucket, and prefix clickhouse_data. Enable s3_force_path_style and set region (регионе) to the bucket's location. The server_id field helps isolate logs for this disk in a multi-tenant setup. This configuration enables upload and read through the GCS endpoint, with data stored under the referenced folder path.
External stage and validation: For orchestration, an external_stage alias can point to the same GCS bucket to simplify data loads from external sources. These settings are referenced by the ingestion pipeline and help ensure consistency for these uploads across users. After установке, perform a test upload to there and verify that the file lands under replica_2 and that queries succeed against the source data. If checks fail, review endpoint accessibility, credentials, and region alignment to ensure the workflow succeeds.
Validate Disk Operations: Write, Read, and Integrity Checks in ClickHouse
Run a three-phase validation after each deployment: Write, Read, and Integrity checks for ClickHouse when using Google Cloud Storage. Target a Mergetree table and a GCS-backed external storage location in us-east1, and verify both data and metadata through every step. Capture metrics from system.query_log and system.parts, then export a summarized report to the downloads folder for audit. This approach helps understandeasytounderstandthumb-upsolved performance and keeps baseline expectations aligned with умолчанию scenarios.
Phase 1 – Write: Create a test dataset and push it through the disk path. Use a table like: CREATE TABLE default.disk_ops_test (id UInt64, label String, ts DateTime, value Float64) ENGINE = MergeTree() ORDER BY ts; Then load a representative load with: INSERT INTO default.disk_ops_test SELECT number, toString(number) AS label, now() AS ts, rand() AS value FROM numbers(1, 5000000); Monitor write throughput by inspecting system.query_log for query_duration_ms, read_rows, and written_rows. Store a compact code-like summary in the description field of the test report and keep the raw data in downloads for later verification. This step confirms that the disk path under external storage handles sustained writes without fragmentation or retries.
Phase 2 – Read: Run a set of representative reads to validate the path back to disk. Execute queries such as: SELECT count(*) AS total, sum(value) AS sum_value, avg(value) AS avg_value FROM default.disk_ops_test; SELECT min(ts) AS first_ts, max(ts) AS last_ts FROM default.disk_ops_test; Compare results against the known baselines saved during the write phase. Track latency and data consistency via system.query_log and by verifying result sets against the precomputed description. Ensure reads remain stable under parallel access and across partitions, especially when merging parts on disk.
Phase 3 – Integrity: Validate per-part metadata and data footprint to catch drift or corruption. Inspect storage metadata with: SELECT table, name AS part, active, rows, bytes_on_disk FROM system.parts WHERE table = 'disk_ops_test' AND active = 1; Confirm that sum(rows) matches the total from write and that bytes_on_disk aligns with the expected footprint. Compute a digest over a representative sample of rows, for example: SELECT md5(concat(toString(id), label, toString(ts), toString(value))) FROM default.disk_ops_test ORDER BY id LIMIT 10000; Store the digest in the metadata and sign it with an hmac-ключ to ensure tamper-evidence. Include a short description of the digest in the report and attach the signature for машине and human verification. Use эти checks to ensure data integrity across external, local, and merged parts.
Automation and cross-cloud validation: Automate these steps as a compact run job that writes a final report to a Google Cloud Storage bucket and mirrors a summary in a local downloads folder. Use external_storage or external_stage integrations to keep test artifacts in one place, and export results to a sink such as BigQuery or a JSON file for archival. If you want to integrate with Pub/Sub alerts, push a notification when the report completes and include a link to the converted (convert) results. Provide an examples dataset to help new users reproduce the test, with a short description of each field and its data type (characters, metadata, and value ranges). The workflow should be easy to understandeasytounderstandthumb-upsolved for operators and engineers.
