24/7 ClickHouse Support
Keep Your Analytical Queries
Running
at Full Speed.
We are Open source Code Contributor
Zero-Day Vulnerability Fixes
Critical Vulnerability Assessment
Roadmap & Recommendations
SLA-Backed Technical Support
Zero-Day Vulnerability Fixes
Critical Vulnerability Assessment
Roadmap & Recommendations
SLA-Backed Technical Support
ClickHouse Support That's Built to Meet the World's Strictest Data Standards
En(AI)blingTM Success for Industry Leaders
ClickHouse Consulting Support Packages
Not every ClickHouse environment carries the same risk. Pick the plan that reflects what a cluster failure actually costs your business.
Standard
Advanced
Platinum
What Ksolves Has Delivered for Organizations Running ClickHouse at Scale
Across fintech, e-commerce, media, and SaaS, enterprises running ClickHouse in production trust Ksolves AI-First approach to deliver faster queries, improved cluster stability, and lower operational costs.
99.99%
SLA Maintained
SLA Maintained
Ksolves holds 99.99% uptime across client environments through proactive monitoring, auto-healing pipelines, and zero-drama incident response.
40%
Lower TCO
Lower TCO
From licensing audits to compute consolidation, Ksolves cuts total cost of ownership by 40%, without cutting corners on performance or reliability.
98%
Contract Renewal Rate
Contract Renewal Rate
We take pride in saying 98% of clients come back. Not because of lock-in, but because the work speaks for itself. That’s Ksolves Promise - on time, on budget, and exactly what was promised.
30 Min
Turnaround Time
Turnaround Time
Ksolves responds and resolves in under 30 minutes, keeping production running and teams unblocked.
End-to-End ClickHouse Support Services for Your Complete Analytical Infrastructure
From the first shard to petabyte-scale multi-replica deployments, Ksolves covers every operational layer of your ClickHouse environment.
24/7 Managed ClickHouse Cluster Operations
ClickHouse performance degrades quietly. Merge queues back up, replicas drift, and parts accumulate until queries break. Ksolves monitors the signals that matter and resolves issues before your data teams feel them.
- Cluster management across single-node, sharded, and multi-replica ClickHouse deployments
- ClickHouse Keeper and ZooKeeper quorum monitoring with leader election tracking and failover management
- Merge health tracking via system.parts with active parts count and merge queue depth alerting
- Replication lag monitoring via system.replication_queue with automated resync on threshold breach
- TTL enforcement, partition pruning, and cold storage migration for data lifecycle management
- Monthly cluster reviews covering parts trends, replication health, and capacity forecasts
- SLA-backed response with named escalation contacts and a dedicated client Slack channel
Observability Stack and Structured Health Check
ClickHouse system tables tell you everything about your cluster. Most teams never use them. Ksolves turns them into a production observability stack and delivers a one-time diagnostic report that surfaces structural risks before data volume exposes them.
- Prometheus endpoint setup with per-shard and per-replica metric labeling
- Grafana dashboards on system.metrics, system.events, system.asynchronous_metrics, and system.disks
- Slow query alerting via query_log and query_thread_log with configurable latency thresholds
- Replication queue depth monitoring with saturation-based alert escalation
- Alert delivery to Slack, PagerDuty, and OpsGenie with runbook links on every alert
- MergeTree engine audit verifying engine selection matches write pattern and deduplication needs
- Partition key review covering cardinality distribution, hotspot risk, and pruning effectiveness
- Query audit identifying full-scan patterns, slow queries, and projection utilization gaps from query_log
- Delivered as a written report with severity-ranked findings, remediation steps, and projected performance impact
ClickHouse Performance Fixed at the Root
ClickHouse performance problems trace back to schema design, index misalignment, or ingestion patterns, not hardware. Ksolves finds the exact layer where performance breaks and fixes it with measurable before-and-after results.
- Sorting key and primary key redesign aligned with actual production query filter patterns
- Projection creation to eliminate full partition scans on high-frequency query shapes
- Materialized view design for workloads where query-time aggregations cannot scale
- Insert pipeline tuning to reduce parts-per-partition and background merge pressure
- Query profiling using EXPLAIN PLAN, EXPLAIN PIPELINE, and system.query_log analysis
ClickHouse Installation and Migration Designed for Production Scale
Shard count, replica placement, and schema decisions made at installation determine how the cluster behaves at 10x current data volume. Ksolves designs ClickHouse environments with that future state in mind and redesigns source schemas before a single row moves.
- Shard and replica topology design based on volume projections, query concurrency, and fault tolerance requirements
- Installation on AWS (EC2, EKS), GCP (GCE, GKE), Azure (VMs, AKS), and on-premises bare metal
- Initial schema design covering engine selection, partition key, sorting key, and index granularity
- Kafka engine setup for real-time ingestion from Apache Kafka and Confluent Platform
- Historical data migration from PostgreSQL, MySQL, Redshift, BigQuery, and Elasticsearch
- Live sync via ClickHouse Kafka engine for streaming and MaterializedMySQL engine for MySQL CDC
- Replica-by-replica rolling version upgrade with zero query downtime across multi-replica clusters
- Post-migration validation covering row counts, query consistency, and performance benchmarking
ClickHouse Security Built Into the Cluster Architecture
ClickHouse holds transaction records, patient data, and financial feeds that auditors scrutinize closely. Ksolves applies security at the architecture level so compliance is a property of how the cluster is built, not a checklist addressed before an audit.
- RBAC configuration covering user profiles, role hierarchies, and privilege grants at the table and column level
- Row-level security policies restricting data visibility by user role on sensitive tables
- TLS across inter-node replication, the native TCP protocol, and the HTTP interface
- LDAP integration for centralized authentication and enterprise single sign-on
- Audit logging via query_log with SIEM export for GDPR, HIPAA, SOC 2, and PCI-DSS compliance
Through the Client's Lens
Why Ksolves Is a Trusted Choice of Global Teams for ClickHouse Support?
ClickHouse expertise is rare. Engineers who have debugged merge backlogs, redesigned partition schemes, and migrated petabytes of data into ClickHouse in production are rarer still. Ksolves has them.
90%
Client Retention Rate
750+
Projects Successfully
Delivered
NSE & BSE
Publicly Listed
Company
600+
Workforce and still
growing
350+
Certifications
200+
Happy Clients
150K+
Support Hours
Completed
Industries We Help Scale with ClickHouse
Every industry runs ClickHouse differently, and Ksolves tailors support around its unique query volume, data freshness requirements, and operational demands.
Telecom
Ksolves manages real-time telecom analytical environments, handling network event ingestion, CDR query workloads, and ClickHouse cluster management across distributed carrier infrastructure at scale.
Healthcare
With deep experience in HIPAA-compliant ClickHouse deployments, we manage clinical analytics pipelines, patient data ingestion workloads, and audit-ready query environments across healthcare data infrastructure.
E-Commerce
Having worked across e-commerce analytics ecosystems, we keep order analytics, funnel reporting, and customer behaviour query workloads fast and consistent across every product and fulfilment channel.
Fintech
Understanding what fintech analytical platforms demand, we manage ClickHouse environments built for transaction analytics, fraud pattern queries, and regulatory reporting, where query speed and data accuracy are non-negotiable.
Entertainment
Working with entertainment platforms at scale, we support high-throughput ClickHouse clusters handling user engagement analytics, content performance reporting, and recommendation signal aggregation that grows with audience demand.
Manufacturing
With hands-on manufacturing data experience, we manage ClickHouse environments, ingesting shop floor IoT sensor data and MES event streams into time-series tables with TTL-based data tiering and efficient merge management.
Retail
Understanding retail analytics complexity, we manage ClickHouse clusters powering POS analytics, loyalty programme reporting, and unified customer data queries across physical and digital channels in real time.
Banking and Financial Services
As a compliance-aware ClickHouse support vendor, we support banking institutions with encrypted ClickHouse deployments, row-level security policies, and audit-ready query environments for regulatory reporting across jurisdictions.
Logistics and Supply Chain
With proven logistics data experience, we manage ClickHouse clusters covering shipment analytics, warehouse telemetry queries, and carrier performance reporting with sub-second query latency for operational dashboards.
Technology and SaaS
Working alongside technology companies, we support ClickHouse clusters powering multi-tenant product analytics, usage metering, and internal business intelligence across cloud-native infrastructure without disruption.
Ksolves on Clickhouse: Insights from Enterprise Experts
Read the latest trends, best practices, and actionable insights shaping modern enterprise technology.
Success Stories from Global Enterprises
Discover real-world case studies showcasing measurable outcomes, faster performance, and successful digital transformation journeys.
HDP to Apache Bigtop Migration with DR Setup
Challenge
A 50+ node HDP 2.6.3 cluster hosting 200 TB hit end-of-life with no vendor patches, no upgrade path, and a single data center — zero disaster recovery.
Solution
Blue-green migration to Apache Bigtop on new hardware in parallel with live cluster, plus cross-site DR across Bangalore and Hyderabad — zero downtime throughout.
200 TB
Migrated — Zero Downtime, Zero Data Loss
Multi-Site CDR Pipeline for a Telecom Operator Across 4 Remote Locations
Challenge
CDR data from 4 remote sites had no unified ingestion- billing reconciliation was fully manual, causing revenue leakage as subscriber volumes grew.
Solution
NiFi agents at all 5 sites feed Kafka → Spark → Druid, with live Superset dashboards for billing and network teams.
Sub-second
Query Response on Live CDR Data
NiFi 1.27 → 2.7 Kubernetes Migration- Financial Services
Challenge
NiFi 1.27 is running on bare metal with no SSO, no scalability, and a growing compliance pipeline that the architecture couldn't support.
Solution
Migrated to NiFi 2.7 on Kubernetes with OneLogin SSO integration, zero downtime, completed in 6 weeks.
3X
Scalability Headroom - 6 Weeks, Zero Downtime
Eliminating ~900K Duplicate Oil Well Records via Azure Databricks
Challenge
The same wellbore appeared under 3–4 different IDs across 6,200 Excel files and 8 systems, causing royalty errors and a BLM audit risk.
Solution
Azure Databricks + PySpark deduplication with geospatial blocking and an ML model (F1=0.971), plus a human-in-the-loop MDM review portal.
~900K
Duplicate Records Eliminated
Petabyte CDR Migration from MapR to ClickHouse -Zero Data Loss
Challenge
Years of CDR data on an end-of-life MapR platform with no vendor support. Compliance queries took 4–6 hours, and regulators required signed proof of zero data loss.
Solution
Spark migrated data in resumable batches with 4 automated validation checks per batch. NiFi produced a signed migration certificate. ClickHouse was optimised for compliance queries from day one.
<8s
Compliance Query Time (from 4–6 hours)
AI-Ready Open Lakehouse on Red Hat OpenShift- Gulf Retailer
Challenge
SAP S/4HANA was too expensive. Cloud platforms unavailable across GCC. 16 TB of daily data needed sub-second processing, and Power BI reports couldn't be touched.
Solution
On-premises lakehouse on existing OpenShift: NiFi → Kafka → Flink → Iceberg on MinIO → Trino serving Power BI as a drop-in SAP BW replacement. Zero new hardware.
16 TB
Daily Data: Sub-Second SLA, Zero New Hardware
HDP to Apache Bigtop Migration with DR Setup
Challenge
A 50+ node HDP 2.6.3 cluster hosting 200 TB hit end-of-life with no vendor patches, no upgrade path, and a single data center — zero disaster recovery.
Solution
Blue-green migration to Apache Bigtop on new hardware in parallel with live cluster, plus cross-site DR across Bangalore and Hyderabad — zero downtime throughout.
200 TB
Migrated — Zero Downtime, Zero Data Loss
Multi-Site CDR Pipeline for a Telecom Operator Across 4 Remote Locations
Challenge
CDR data from 4 remote sites had no unified ingestion- billing reconciliation was fully manual, causing revenue leakage as subscriber volumes grew.
Solution
NiFi agents at all 5 sites feed Kafka → Spark → Druid, with live Superset dashboards for billing and network teams.
Sub-second
Query Response on Live CDR Data
NiFi 1.27 → 2.7 Kubernetes Migration- Financial Services
Challenge
NiFi 1.27 is running on bare metal with no SSO, no scalability, and a growing compliance pipeline that the architecture couldn't support.
Solution
Migrated to NiFi 2.7 on Kubernetes with OneLogin SSO integration, zero downtime, completed in 6 weeks.
3X
Scalability Headroom - 6 Weeks, Zero Downtime
Eliminating ~900K Duplicate Oil Well Records via Azure Databricks
Challenge
The same wellbore appeared under 3–4 different IDs across 6,200 Excel files and 8 systems, causing royalty errors and a BLM audit risk.
Solution
Azure Databricks + PySpark deduplication with geospatial blocking and an ML model (F1=0.971), plus a human-in-the-loop MDM review portal.
~900K
Duplicate Records Eliminated
Petabyte CDR Migration from MapR to ClickHouse -Zero Data Loss
Challenge
Years of CDR data on an end-of-life MapR platform with no vendor support. Compliance queries took 4–6 hours, and regulators required signed proof of zero data loss.
Solution
Spark migrated data in resumable batches with 4 automated validation checks per batch. NiFi produced a signed migration certificate. ClickHouse was optimised for compliance queries from day one.
<8s
Compliance Query Time (from 4–6 hours)
AI-Ready Open Lakehouse on Red Hat OpenShift- Gulf Retailer
Challenge
SAP S/4HANA was too expensive. Cloud platforms unavailable across GCC. 16 TB of daily data needed sub-second processing, and Power BI reports couldn't be touched.
Solution
On-premises lakehouse on existing OpenShift: NiFi → Kafka → Flink → Iceberg on MinIO → Trino serving Power BI as a drop-in SAP BW replacement. Zero new hardware.
16 TB
Daily Data: Sub-Second SLA, Zero New Hardware
Frequently Asked Questions
Everything you need to know before choosing a ClickHouse support partner.
ClickHouse managed services cover 24×7 cluster monitoring, replication lag management, merge backlog alerting, query optimization, version upgrades, security hardening, and incident response with full root cause analysis.
The most common cause is a primary key ordering that does not align with your filter columns, forcing the sparse index to scan unnecessary granules. Missing projections, ineffective column pruning, and high active parts counts from poorly batched inserts are also frequent contributors.
Replication lag typically occurs when ZooKeeper or ClickHouse Keeper sessions time out under write load, when the background replication thread pool is saturated, or when large part sizes slow inter-replica transfers. Network bandwidth between nodes compounds the problem in multi-shard setups.
Migration starts with a schema audit and MergeTree table redesign based on your query patterns. Ksolves builds the historical migration pipeline, configures live sync using the Kafka engine or MaterializedMySQL engine, and validates data consistency before cutover.
MergeTree suits append-heavy workloads. ReplacingMergeTree handles upserts by deduplicating on the sorting key during merges. AggregatingMergeTree stores partial aggregation states for materialized views. CollapsingMergeTree and VersionedCollapsingMergeTree manage row cancellation for mutable datasets. The right choice depends on how your data changes after it is written.
ClickHouse performs best with large batched inserts between 10,000 and 1,000,000 rows. Small, frequent inserts create too many parts and saturate the merge process. For streaming workloads, async_insert mode or a Buffer table engine absorbs high-frequency writes and flushes them as properly sized batches.
ClickHouse supports single-node deployments, sharded multi-replica clusters coordinated by ClickHouse Keeper, and fully managed deployment via ClickHouse Cloud. Ksolves supports all deployment models across AWS, GCP, Azure, and on-premises infrastructure.
BigQuery and Redshift are managed cloud warehouses with per-query or per-slot pricing that scales expensively at high query volumes. ClickHouse uses vectorized execution on local column storage to deliver sub-second analytical query latency at significantly lower cost per query, making it the stronger choice for high-throughput real-time analytics.
Yes. Ksolves is a trusted ClickHouse support vendor serving enterprises across North America with ClickHouse consulting services, ClickHouse enterprise support, and 24×7 global coverage with sub-15-minute critical incident SLAs.
Merge backlog builds when parts accumulate faster than background merge threads can process them, almost always from high-frequency small inserts. Fixes include increasing INSERT batch sizes, tuning background_pool_size, adjusting max_bytes_to_merge_at_max_space_in_pool, and redesigning the partition key to distribute write volume more evenly.
