Feeds

Google Percolator – global search jolt sans MapReduce comedown

The machine that brews the Caffeine

Choosing a cloud hosting partner with confidence

Death to stragglers

In a Percolator cluster, three pieces run on each machine: a Percolator worker, a BigTable tablet server, and a GFS chunkserver. With GFS, master nodes oversee data spread across a series of distributed chunkservers, which store, yes, chunks of data. Observers hook into the Percolator worker, and the worker interfaces with BigTable. GFS, as Lipokovitz explained, is the database's underlying storage engine.

Whereas MapReduce nabbed all of the data for tens or even hundreds of webpages, Percolator executes roughly fifty BigTable operations when processing a single document.

Google Percolator setup

Percolator applications are essentially a series of observers. Each observer completes a task and passes more work onto the next observer by writing to the table. There are relatively few observers per app: Caffeine uses about 10. Because the system can operate without rescanning the entire index, it's much simpler than the 100-MapReduce indexing setup of the past. And with latency reduced, Google can expand the size of its index. Caffeine's collection of documents is three times larger than that used by the old MapReduce system.

The size of the system, Google engineers say, is limited only by the available disk space.

Percolator also avoids the MapReduce "straggler" problem, where a few slow operations can hold up the entire process, and according to the Google engineers, it's easier to operate. "In the old system, each of a hundred different MapReduces needed to be individually configured and could independently fail. Also, the 'peaky' nature of the MapReduce workload made it hard to fully utilize the resources of a datacenter compared to Percolator’s much smoother resource usage."

The rub is that Caffeine uses roughly twice the resources to keep up with the same crawl rate. According to the paper, Percolator performance lies somewhere between that of MapReduce and a traditional database management system (DBMS). "Because Percolator is a distributed system, it uses far more resources to process a fixed amount of data than a traditional DBMS would; this is the cost of its scalability. Compared to MapReduce, Percolator can process data with far lower latency, but again, at the cost of additional resources required to support random lookups."

Google Percolator benchmarks

According to Peng and Dabek, the performance of the system will scale almost linearly a resources are added, as indicated by tests with the industry standard TPC-E benchmark. But that added overhead may be an issue. "The system achieved the goals we set for reducing the latency of indexing a single document with an acceptable increase in resource usage compared to the previous indexing system," the paper concludes.

"The TPC-E results suggest a promising direction for future investigation. We chose an architecture that scales linearly over many orders of magnitude on commodity machines, but we’ve seen that this costs a significant 30-fold overhead compared to traditional database architectures. We are very interested in exploring this tradeoff and characterizing the nature of this overhead: how much is fundamental to distributed storage systems, and how much can be optimized away?" ®

Security for virtualized datacentres

More from The Register

next story
It's Big, it's Blue... it's simply FABLESS! IBM's chip-free future
Or why the reversal of globalisation ain't gonna 'appen
'Hmm, why CAN'T I run a water pipe through that rack of media servers?'
Leaving Las Vegas for Armenia kludging and Dubai dune bashing
Bitcasa bins $10-a-month Infinite storage offer
Firm cites 'low demand' plus 'abusers'
Facebook slurps 'paste sites' for STOLEN passwords, sprinkles on hash and salt
Zuck's ad empire DOESN'T see details in plain text. Phew!
CAGE MATCH: Microsoft, Dell open co-located bit barns in Oz
Whole new species of XaaS spawning in the antipodes
Microsoft and Dell’s cloud in a box: Instant Azure for the data centre
A less painful way to run Microsoft’s private cloud
prev story

Whitepapers

Why cloud backup?
Combining the latest advancements in disk-based backup with secure, integrated, cloud technologies offer organizations fast and assured recovery of their critical enterprise data.
A strategic approach to identity relationship management
ForgeRock commissioned Forrester to evaluate companies’ IAM practices and requirements when it comes to customer-facing scenarios versus employee-facing ones.
Security for virtualized datacentres
Legacy security solutions are inefficient due to the architectural differences between physical and virtual environments.
Reg Reader Research: SaaS based Email and Office Productivity Tools
Read this Reg reader report which provides advice and guidance for SMBs towards the use of SaaS based email and Office productivity tools.
New hybrid storage solutions
Tackling data challenges through emerging hybrid storage solutions that enable optimum database performance whilst managing costs and increasingly large data stores.