Slicer: Auto-Sharding for Datacenter Applications
Abstract
Sharding is a fundamental building block of large-scale applications, but most have their own
custom, ad-hoc implementations. Our goal is to make sharding as easily reusable as a filesystem or
lock manager. Slicer is \Google's general purpose sharding service. It monitors signals such as
load hotspots and server health and dynamically shards work over a set of servers. Its goals are to
maintain high availability and reduce load imbalance while minimizing churn from moved work.
In this paper, we describe Slicer's design and implementation. Slicer has the consistency and global
optimization of a centralized sharder while approaching the high availability, scalability, and low
latency of systems that make local decisions.
It achieves this by separating concerns:
a reliable data plane forwards requests, and
a smart control plane makes load-balancing decisions off the critical path.
Slicer's small but powerful API has proven useful and easy to adopt
in dozens of \Google applications.
It is used to allocate resources for web service front-ends,
coalesce writes to increase storage bandwidth, and
increase the efficiency of a web cache.
It currently handles 2-6M~req/s of production traffic.
Production workloads using Slicer
exhibit a most-loaded task 30\%--180\% of the mean load,
even for highly skewed and time-varying loads.
custom, ad-hoc implementations. Our goal is to make sharding as easily reusable as a filesystem or
lock manager. Slicer is \Google's general purpose sharding service. It monitors signals such as
load hotspots and server health and dynamically shards work over a set of servers. Its goals are to
maintain high availability and reduce load imbalance while minimizing churn from moved work.
In this paper, we describe Slicer's design and implementation. Slicer has the consistency and global
optimization of a centralized sharder while approaching the high availability, scalability, and low
latency of systems that make local decisions.
It achieves this by separating concerns:
a reliable data plane forwards requests, and
a smart control plane makes load-balancing decisions off the critical path.
Slicer's small but powerful API has proven useful and easy to adopt
in dozens of \Google applications.
It is used to allocate resources for web service front-ends,
coalesce writes to increase storage bandwidth, and
increase the efficiency of a web cache.
It currently handles 2-6M~req/s of production traffic.
Production workloads using Slicer
exhibit a most-loaded task 30\%--180\% of the mean load,
even for highly skewed and time-varying loads.
Research Areas
