PowerScale | Understanding L3 Cache and Metadata Strategies

Understanding L3 Cache:

L3 Cache: L3 cache is a secondary level of cache that resides on SSDs, supplementing the primary memory cache (L1 and L2). It operates as an eviction cache, storing frequently accessed data and metadata to improve read latency. L3 cache is most beneficial for workflows involving random file access. It can operate in a metadata-only mode for archive-series storage nodes. Enabling L3 cache on a node pool with existing data on SSDs requires the drives evacuate that data to HDDs before the SSDs can be used for caching. Disabling the L3 cache is generally a faster operation.

Workflows That Benefit from L3 Cache: 

• • L3 cache is beneficial for workflows with the following characteristics:
  • Random File Access: Workloads that involve frequent reads of different, non-sequential parts of files can see significant latency reductions with L3 cache.
  • High Read-to-Write Ratio: Since L3 cache primarily accelerates reads, workflow with a dominant read component benefits the most.
  • Caching of Frequently Accessed "Hot" Data: L3 cache automatically identifies and stores frequently accessed data, improving performance for repeated access.
  • Streaming and Concurrent File Access (to some extent): While random access sees the most benefit, workflows with streaming and concurrent access can also experience some performance improvements with L3 cache.

When to Choose L3 Cache:

• • When the primary performance bottleneck is random read latency for both data and metadata.
  • To extend the effective memory capacity of nodes without incurring the cost of more RAM.
  • For workloads that exhibit a significant amount of re-reading of data and metadata that has been recently evicted from L2.
  • For archive-class nodes, where metadata performance for file system traversal is critical.
  • When a simpler, "set and forget" read performance enhancement is wanted without significant configuration overhead.
    When to Choose Metadata Acceleration:
  • When metadata operations (lookups, access, modifications) are the primary performance bottleneck.
  • For workloads with a high volume of metadata reads (metadata read acceleration) or both reads and writes (metadata read/write acceleration).
  • In scenarios like seismic interpretation where fast metadata access is paramount, even if the underlying data resides on slower storage.
  • When granular control over where metadata resides is required.
  • When extending metadata read benefits to nodes without local SSDs is necessary (using GNA with metadata read acceleration on other nodes).
  • Workloads such as home directories, workflows with heavy file enumeration, and activities requiring numerous comparisons often exhibit high metadata read activity. In such cases, accelerating metadata access directly can lead to significant performance improvement

Understanding Metadata Strategies:

Metadata Strategy: Instead of caching data, SSDs can be configured to primarily store and accelerate metadata operations. This strategy can be beneficial for workloads with a high volume of metadata access, such as many small files, frequent directory lookups, and metadata-intensive job engine tasks. OneFS supports different metadata SSD strategies, including metadata-read and metadata-write.

Metadata-Read: SSDs are primarily used to accelerate metadata read operations.

Metadata-Write: SSDs are used to accelerate metadata write operations. 

• Benefits of Metadata Strategy Over L3 Cache:
• Metadata acceleration offers more targeted and granular control over how SSDs are used to enhance metadata performance for specific datasets and workflows. L3 cache, on the other hand, is a more general caching layer that benefits a broader range of workloads, particularly those with repeated random read access to both data and metadata. While L3 cache excels at improving read performance for frequently accessed data, a dedicated metadata strategy can offer specific advantages: 
  • Improved Metadata Performance: For workloads where metadata operations are the bottleneck (e.g., opening, closing, renaming, listing large numbers of files), dedicating SSDs to metadata can significantly reduce latency and improve overall throughput.
  • Enhanced Job Engine Performance: Certain OneFS job engine tasks are metadata-intensive. Accelerating metadata access can lead to faster completion times for these jobs.
  • Predictable Performance for Metadata-Heavy Workloads: In environments with a consistent pattern of high metadata activity, a dedicated metadata strategy can provide more predictable and sustained performance improvements compared to an eviction-based cache.
  • Certain applications and workflows generate a disproportionately high number of metadata operations compared to actual data reads and writes. Examples include file archiving, media asset management, electronic design automation (EDA), software development environments with frequent compilations, and genomics pipelines that involve numerous small file accesses and analyses. In these cases, the latency associated with accessing and manipulating metadata can become a significant performance bottleneck
  • Operations that involve navigating complex directory structures or listing the contents of many directories are heavily dependent on metadata performance. Metadata acceleration ensures that the system can quickly access the inode information and directory entries, significantly speeding up these operations compared to relying on even an L3 cache that might have evicted this information due to capacity constraints or less frequent access
  • Backup, Replication, and Migration: These data management tasks often involve extensive metadata scanning and processing. Faster metadata access through acceleration can significantly reduce the time required to complete these jobs, minimizing disruption to primary workloads and improving operational efficiency.
  • Search and Indexing: When users or automated processes must search for specific files based on their metadata attributes (e.g., name, size, modification date), accelerated metadata access allows for faster query execution. This is relevant for solutions like MetadataIQ, which indexes file system metadata for efficient querying and data discovery across multiple clusters
• When to choose Metadata: 
  • Heavy Directory browsing, File or data search operations, Indexing.
  • File operations like opening, closing, deleting, creating directories (mkdir).
  • Lookup, getattr, and access operations.
  • Home directories, especially those with many objects.
  • Workflows involving heavy enumeration or comparisons.
  • Seismic data interpretation, where metadata timeliness is critical.
  • Metadata acceleration can yield significant performance improvements for these types of activities, increasing throughput and decreasing latency

Summary: When to Choose

• • Choose a Metadata Acceleration strategy (Metadata Read or Metadata Read/Write) if your workload is heavily biased towards operations that access or modify file metadata (browsing, searching, indexing, creating, deleting, modifying attributes).
  • Choose Metadata Read Acceleration if your workload is primarily metadata read-intensive and you want to use less SSD capacity.
  • Choose Metadata Read/Write Acceleration if your workload involves a significant amount of metadata writes, requires faster snapshot deletes, or is a small file HPC workload like EDA benefiting from inlined small files on flash. Ensure that you have sufficient SSD capacity.
  • Consider GNA if you have a mixed cluster (nodes with and without SSDs) and must accelerate metadata reads for data residing on non-SSD nodes across the cluster. This is relevant for metadata-intensive workloads that are spread out.
    • Global namespace Acceleration (GNA): GNA is an older mechanism (intended to be replaced by L3 cache when all nodes have SSDs) that allows node pools without SSDs to leverage SSDs elsewhere in the cluster by storing extra metadata mirrors on those SSDs. This accelerates metadata read operations for data stored on HDD-only pools. L3 cache and GNA can co-exist in the same cluster but typically operate on different node pools.
  • Consider L3 Cache if your workload involves significant random reads, benefits from extended caching for a large working set, or needs improved Job Engine performance, provided your nodes have SSDs.

Tools and commands:

• Performance Monitoring: Use tools like InsightIQ, CloudIQ, and MetadataIQ for monitoring cluster health, performance metrics, and usage forecasting. InsightIQ can track performance trends, identify patterns, and perform file analytics. It can also help estimate when a cluster reaches maximum capacity. CloudIQ provides insights into cluster performance. MetadataIQ facilitates data indexing and querying across clusters and can be used for data life cycle management and understanding data distribution.
• The isi_cache_stats utility can help determine the working dataset size, which is relevant for sizing SSDs for L2 and L3 cache. A general rule suggests that L2 capacity + L3 capacity should be >= 150% of the working set size.
• MetadataIQ (OneFS 9.10+): Deploy and configure MetadataIQ to index and create a global catalog of metadata across clusters. Use the Kibana dashboard to visualize data distribution, file counts, and metadata attributes. This helps understand the composition of your data and how metadata is growing. Periodic synchronizations keep the metadata database updated
• InsightIQ provides reports on cluster capacity, including total, provisioned, and used capacity, allowing you to forecast storage needs based on historical trends. It can monitor workload performance, latency, IOPS, and throughput, allowing you to detect potential bottlenecks as data grows. InsightIQ's File System Analytics reports can show file count and size distribution, giving you insight into the scale and composition of your data, which directly relates to LIN count growth.