Recommandation: 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 paramètre 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 | Recommandation | Example |
|---|---|---|
| Naming | Use a structured pattern including project, env, region, and domain | myproj-prod-регионе-products |
| Cycle de vie | 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 | Définir storage_allowed_locations sur à la fois régional et multi-régional | storage_allowed_locations: both |
Pour les modèles et les exemples, informationsamples fournissent des modèles concrets que vous pouvez adapter à vos projets, garantissant que les règles couvrent la création de бакет, la gestion des objets et les contrôles d'accès côté utilisateur.
Assigner des rôles IAM et des comptes de service pour l'accès à ClickHouse
Recommandation: Créez un compte de service dédié pour ClickHouse et liez-le à bucket_name avec les rôles minimums nécessaires pour effectuer les opérations requises. Cela isole le хранение et réduit l'exposition dans l'ensemble du projet, tout en permettant un accès prévisible pour votre workload ClickHouse.
Dans votre projet GCP, nommez l'SA clickhouse-storage-sa et ajoutez une description like "ClickHouse access to GCS." Record the created datetime et référez-vous à cette entrée dans votre politique de sécurité. La même SA sera utilisée par votre déploiement ClickHouse, et vous pouvez réutiliser son identité entre les composants pour une approche cohérente connecté flow.
In this section, configurez l'accès sur le bucket_name avec un least-privilege approach. Accordez au bucket les rôles suivants : roles/storage.objectViewer for download et opérations de lecture, et roles/storage.objectCreator to permit object moveobject et des uploads. Si vous devez overwrite or delete objects, add roles/storage.objectAdmin mais restreindre la portée à ce bucket afin de maintenir un same niveau de contrôle et minimiser les risques. Ceci specific combination supports both reading data and writing new objects with enabled frontières de sécurité.
Pour éviter les clés statiques, associez la charge de travail ClickHouse à l'aide de la fédération d'identité de charge de travail. Associez le compte de service Kubernetes utilisé par ClickHouse au SA GCP afin que les informations d'identification restent. connecté sans distribuer de secrets. Après le mapping, vérifiez que le processus ClickHouse performs data fetches against bucket_name using the bound identity, and confirm the path aligns with your definition de contrôle d'accès.
Inclure une clause de politique qui restreint le SA à bucket_name et à un chemin défini à l'intérieur de ce bucket. Utiliser un dédié clause pour prévenir l'accès inter-buckets et s'assurer que la politique est referenced par votre déploiement. Cela maintient une limite d'accès stricte et simplifie l'audit pour votre équipe et pour les workflows d'audit des utilisateurs.
Activer les journaux d’audit Cloud pour storage.googleapis.com et surveiller les événements d’accès. Associer chaque événement à la description and name du compte de service afin de pouvoir identifier rapidement les actions qui ont été effectué by ClickHouse. Les journaux afficheront qui a accédé à bucket_name, quelle opération s'est produite (téléchargement, déplacement d'objet ou suppression), et le datetime de l'activité. Cela fournit une trace claire pour la conformité et la réponse aux incidents, en particulier lorsque будет est détecté un accès inhabituel.
Testez la configuration en effectuant une séquence ciblée : authentifiez-vous auprès de ClickHouse SA, exécutez une download from bucket_name, puis lancez un moveobject effectuer une opération au sein du même bucket, puis vérifier que l'objet existe avec les métadonnées attendues. Confirmer que les actions apparaissent dans le journal d'audit sous le même compte de service et que les permissions le reflètent. full cycle de vie des objets impliqués. Utilisez ces tests pour valider que le section les exigences sont satisfaites et que votre your politiques maintenues sous une charge réelle.
Si vous devez accorder l'accès aux utilisateurs de votre organisation, attachez les mêmes rôles IAM à un compte de service lié à un groupe ou créez un second SA avec une portée définie sur un bucket séparé avec les mêmes description conventions. Conserver le name and created champs alignés pour que la politique reste referenced à travers les équipes. Cette approche garantit un accès contrôlé tout en évitant les informations d'identification en double et en maintenant un historique clair et consultable des modifications.
Cette stratégie aboutit à un modèle pratique et sécurisé pour l'intégration de Google Cloud Storage et de ClickHouse, avec des limites claires, des événements auditables et un processus reproductible que vous pouvez réutiliser pour d'autres buckets ou charges de travail dans votre environnement. Utilisez ceci section servira de modèle pour les déploiements et mises à jour futurs de votre configuration IAM.
Configurer ClickHouse pour utiliser GCS comme disque via un point de terminaison compatible S3
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.
