Fragmentation in Large Object Repositories Russell Sears Catharine van Ingen CIDR 2007 This work was performed at Microsoft Research San Francisco with input from the NTFS and SQL Server teams

Background Web services store large objects for users –eg: Wikipedia, Flickr, YouTube, GFS, Hotmail Replicate BLOBs or files –No update-in-place Benchmark before deployment –Then, encounter storage performance problems We set out to make some sense of this Object Stores DB (metadata) Application Servers Replication / Data scrubbing Clients

Problems with partial updates Multiple changes per application request –Atomicity (distributed transactions) Most updates change object size –Must fragment, or relocate data Reading / writing the entire object addresses these issues

Experimental Setup Single storage node Compared filesystem, database –NTFS on Windows Server 2003 R2 –SQL Server 2005 beta Repeatedly update (free, reallocate) objects –Randomly chose sizes, objects to update –Unrealistic, easy to understand Measured throughput, fragmentation

Reasoning about time Existing metrics –Wall clock time: Requires trace to be meaningful, cannot compare different workloads –Updates per volume: Coupled to volume size Storage Age: Average number of updates per object

Read performance Clean system –SQL good small object performance (inexpensive opens) –NTFS significantly faster with objects >>1MB SQL degraded quickly NTFS small object performance was low, but constant NTFSSQLNTFSSQL Read Throughput (MB/s) 024 Updates per object 256 KB Objects1 MB Objects

10MB object fragmentation Storage Age Fragments/object SQL Server NTFS NTFS approaching asymptote SQL Server degrades linearly –No BLOB defragmenter

Rules of Thumb Classic pitfalls –Low free space (< 10%) –Repeated allocation and deallocation (High storage age) One new problem –Small volumes (< x object size) Implicit tuning knobs –Size of write requests

Append is expensive! Neither system can take advantage of final object size during allocation Both API’s provide “append” –Leave gaps for future appends –Place objects without knowing length Observe same behavior with single and random object sizes

Conclusions Get/put storage is important in practice Storage age –Metric for comparing implementations and workloads –Fragmentation behaviors vary significantly Append leads to poor layout

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Theory vs. Practice Theory focuses on contiguous layout of objects of known size Objects that are allocated in groups are freed in groups –Good allocation algorithms exploit this –Generally ignored for average case results –Leads to pathological behavior in some cases

Small objects / Large volumes –Percent free space Large objects / Small volumes –Number of free objects Small volumes

Efficient Get/Put No update-in-place –Partial updates complicate apps –Objects change size Pipeline requests –Small write buffers, I/O Parallelism Application server 1234

Lessons learned Target systems avoid update-in-place No use for database data models Quantified fragmentation behavior –Across implementations, workloads Common API’s complicate allocation –Filesystem / BLOB API is too expressive

Application server 1234

Example systems SharePoint –Everything in the database, one copy per version Wikipedia –One blob per document version; images are files Flickr / YouTube GFS –Scalable append; chunk data into 64MB files Hotmail –Each mailbox is stored as a single opaque BLOB

The folklore is accurate, so why do application designers… …benchmark, then deploy the “wrong” technology? …switch to the “right one” a year later? …then switch back?!? Performance problems crop up over time

Conclusions Existing systems vary widely –Measuring clean systems is inadequate, but standard practice Support for append is expensive Unpredictable storage is difficult to reliably scale and manage –See paper for more information about predicting and managing fragmentation in existing systems

Comparing data layout strategies Study the impact of –Volume size –Object size –Workload –Update strategies –Maintenance tasks –System implementation Need a metric that is independent of these factors

Related work Theoretical results –Worst case performance is unacceptable –Average case good for certain workloads –Structure in deallocation requests leads to poor real-world performance Buddy system –Place structural limitations on file layout –Bounds fragmentation, fails on large files

Introduction Content-rich web services require large, predictable and reliable storage Characterizing fragmentation behavior Opportunities for improvement

Data intensive web applications Simple data model (BLOBs) –Hotmail: user mailbox –Flickr: photograph(s) Replication –Instead of backup –Load balancing –Scalability Object Stores DB (metadata) Application Servers Replication / Data scrubbing Clients

Databases vs. Filesystems Manageability should be primary concern –No need for advanced storage features –Disk bound Folklore –File opens are slow –Database interfaces stream data poorly

Clean system performance K512K1M Object Size Read throughput (MB/sec) SQL Server NTFS Single node –Used network API’s Random workload –Get/put one object at a time Large objects lead to sequential I/O

Revisiting Fragmentation Data intensive web services –Long term predictability –Simple data model: get/put opaque objects Performance of existing systems Opportunities for improvement

Introduction Large object updates and web services –Replication for scalability, reliability –Get / put vs. partial updates Storage age –Characterizing fragmentation behavior –Comparing multiple approaches State-of-the-art approach: –Lay out data without knowing final object size –Change the interface?