Optimizing Cloudera workloads can be challenging, especially when running on-prem or in hybrid infrastructures. Engineering teams deal with fluctuating workloads, fragmented teams and tools, complex infrastructure, custom configurations, and more. Yet, enterprise data engineers are under pressure to deliver while often hindered by limited visibility and time-consuming manual processes.
1. Dynamic Workload Fluctuations
A major obstacle when optimizing Cloudera is managing spikes and dips in workloads. As usage patterns shift, clusters experience resource contention. The right database applications can handle ad-hoc, concurrent queries initially, though performance suffers as memory, CPU, and I/O saturation occur.
The dynamic nature of these fluctuations makes them difficult to anticipate, creating significant challenges in appropriate resource allocation. For example, clusters sized for average workloads can struggle during production or require mid-project adjustments. Yet, scaling for peak usage can result in clusters that are unused or underutilized in normal conditions — creating significant waste and ratcheting up costs unnecessarily. The same applies to clusters that are spun up and then abandoned after projects conclude without being shut down. It’s like leaving the tap open on a water spigot — only instead of water flowing, it’s money.
It is a tedious task for engineers to manually tune cluster configurations and infrastructure size to account for shifting workloads. On top of that, it’s almost impossible to achieve this reactively when confronting acute spikes. There are simply too many interdependent variables to consider, from task slots, to cache sizes, to query pools, and more.
The lag to identify, diagnose, and resolve makes performance targets difficult to consistently meet at enterprise scale.
2. Fragmented Teams and Tools
Data teams also struggle to optimize performance due to disparate monitoring tools and disjointed teams managing separate components.
While Cloudera Manager provides metrics on Hadoop, Impala and Spark workloads, storage teams often depend on UIs from vendors like NetApp and Dell EMC for their SAN and NAS arrays. Virtualization admins may track VM performance via vCenter. Network teams might monitor using SolarWinds. The list grows longer with every new tool in the mix.
This fragmentation often results in no way to view the overall health of end-to-end workloads. Engineers optimize the parts they see, whether that’s troubleshooting a slow query, adding datastore capacity during a storage latency spike, or rebooting a top-of-rack switch that is dropping packets. These reactive, siloed actions often shift bottlenecks elsewhere as resource allocations shift.
Without proper collaboration or a unified view of how changes impact other layers in the stack, optimizations miss the bigger picture. For enterprises with custom configurations, this proves especially problematic. There are simply too many interdependent variables and relationships that most tools don’t account for.
3. Customized Configurations
The scale and uniqueness of large on-prem infrastructure brings its own complications. The exact combination of operating systems, hardware specs, network architectures, and virtualization platform versions are likely specific to each organization. These highly customized production environments have typically evolved over many years to support legacy applications or processes.
The intricacies make it significantly harder to model the impact of system changes or workload optimizations holistically. For example, standing up new physical servers with shiny NVMe storage won’t speed up Spark jobs if the cluster’s underlying 10Gb network fabric is saturated. Yet, without clear visibility, engineers can waste time and resources bolting on siloed hotfixes that may not move the needle.
When making tuning changes across layers like code, operating systems, virtualization platforms, and hardware, there are plenty of dependencies — which can create unintended consequences. Even well-intentioned optimizations like changing allocation might “starve” other critical cluster services if applied without visibility into adjacencies. With an end-to-end way to manage resources efficiently, the risk of misconfiguration or instability increases substantially.
4. Tribal Knowledge and Manual Processes
To contend with the enormity of enterprise infrastructure complexity, siloed teams often depend on the tribal knowledge of tenured engineers. For example, storage teams might develop homegrown scripts that collect custom VMware’s ESXi hypervisor metrics to hand-tune SAN LUN optimizations. Network engineers tweak proprietary TCP stack settings to improve throughput based on past experience. Hadoop architects adjust values in low-level configuration files to balance MR jobs.
Even with experienced teams, however, troubleshooting issues or improving performance is a labor-intensive manual process. Engineers can spend significant time identifying and validating proper tuning changes across each layer. Many are forced to rely on instinct due to limited data access to prove the direct impact of alteration.
Evolving systems and staff turnover only exacerbate the situation, especially when there is not a strong culture that prioritizes optimization.
These manual processes also take a toll on efficient usage and innovation. Daily fire drills from user tickets or outage alerts get priority at the expense of proactive and strategic fine-tuning based on usage analytics and anticipated demand.
5. Critical Performance SLAs
The impact of these optimization challenges plays out in the business itself. Cloudera workloads often underpin vital processes. Lags or instability can easily translate directly to lost revenue or unhappy customers. Data engineers face immense pressure to deliver peak performance against aggressive SLAs amid a complex enterprise infrastructure.
The inertia of legacy systems, lack of holistic visibility, and reliance on manual processes keep critical analytics infrastructure from running as efficiently as modern businesses demand.
Overcoming Cloudera Optimization Challenges
As data volumes continue to grow — along with ever-increasing performance expectations — a different way of approaching these challenges is required. Data engineers need an automated system that provides a higher level of visibility to guide workload optimization based on real-time data.
There are a number of Cloudera optimization best practices, ranging from choosing the right instance type or implementing cluster auto-scaling. However, depending where you are in your optimization journey, Intel Granulate can be a great solution, providing continuous and secure Cloudera workload optimization in real-time. Working autonomously, Intel Granulate improves Cloudera application performance, increases throughput, and manages costs without any code change required.
Dell was able to accelerate their ML and DL pipelines on Cloudera with Intel Granulate, leading to improved performance for their PowerEdge Servers on the data platform. Learn more about how Intel Granulate can help optimize the capacity of your Cloudera workloads autonomously.