Aug. 17, 2021
Here is the paper.
AMENABLE TO EXTENSIVE PARALLELIZATION, GOOGLE’S WEB SEARCH
APPLICATION LETS DIFFERENT QUERIES RUN ON DIFFERENT PROCESSORS AND,
BY PARTITIONING THE OVERALL INDEX, ALSO LETS A SINGLE QUERY USE
MULTIPLE PROCESSORS. TO HANDLE THIS WORKLOAD, GOOGLE’S
ARCHITECTURE FEATURES CLUSTERS OF MORE THAN 15,000 COMMODITYCLASS PCS WITH FAULT-TOLERANT SOFTWARE. THIS ARCHITECTURE ACHIEVES
SUPERIOR PERFORMANCE AT A FRACTION OF THE COST OF A SYSTEM BUILT
FROM FEWER, BUT MORE EXPENSIVE, HIGH-END SERVERS.
Few Web services require as much
computation per request as search engines.
On average, a single query on Google reads
hundreds of megabytes of data and consumes
tens of billions of CPU cycles. Supporting a
peak request stream of thousands of queries
per second requires an infrastructure comparable in size to that of the largest supercomputer installations. Combining more than
15,000 commodity-class PCs with fault-tolerant software creates a solution that is more
cost-effective than a comparable system built
out of a smaller number of high-end servers .
Here we present the architecture of the
Google cluster, and discuss the most important
factors that influence its design: energy efficiency and price-performance ratio. Energy
efficiency is key at our scale of operation, as
power consumption and cooling issues become
significant operational factors, taxing the limits of available data center power densities.
Our application affords easy parallelization:
Different queries can run on different processors, and the overall index is partitioned so
that a single query can use multiple processors. Consequently, peak processor performance is less important than its price/
performance. As such, Google is an example
of a throughput-oriented workload, and
should benefit from processor architectures
that offer more on-chip parallelism, such as
simultaneous multithreading or on-chip multiprocessors.
Google architecture overview
Google’s software architecture arises from
two basic insights. First, we provide reliability
in software rather than in server-class hardware, so we can use commodity PCs to build
a high-end computing cluster at a low-end price. Second, we tailor the design for best
aggregate request throughput, not peak server
response time, since we can manage response
times by parallelizing individual requests.
We believe that the best price/performance
tradeoff for our applications comes from fashioning a reliable computing infrastructure
from clusters of unreliable commodity PCs.
We provide reliability in our environment at
the software level, by replicating services across
many different machines and automatically
detecting and handling failures. This software based reliability encompasses many different
areas and involves all parts of our system
design. Examining the control flow in handling a query provides insight into the high level structure of the query-serving system, as
well as insight into reliability considerations.
Serving a Google query
When a user enters a query to Google (such
as www.google.com/search?q=ieee+society), the
user’s browser first performs a domain name system (DNS) lookup to map www.google.com
to a particular IP address. To provide sufficient
capacity to handle query traffic, our service consists of multiple clusters distributed worldwide.
Each cluster has around a few thousand
machines, and the geographically distributed
setup protects us against catastrophic data center failures (like those arising from earthquakes
and large-scale power failures). A DNS-based
load-balancing system selects a cluster by
accounting for the user’s geographic proximity
to each physical cluster. The load-balancing system minimizes round-trip time for the user’s
request, while also considering the available
capacity at the various clusters.
The user’s browser then sends a hypertext
transport protocol (HTTP) request to one of
these clusters, and thereafter, the processing
of that query is entirely local to that cluster.
A hardware-based load balancer in each cluster monitors the available set of Google Web
servers (GWSs) and performs local load balancing of requests across a set of them. After
receiving a query, a GWS machine coordinates the query execution and formats the
results into a Hypertext Markup Language
(HTML) response to the user’s browser. Figure 1 illustrates these steps.
Query execution consists of two major
phases.1 In the first phase, the index servers consult an inverted index that maps each
query word to a matching list of documents
(the hit list). The index servers then determine
a set of relevant documents by intersecting the
hit lists of the individual query words, and
they compute a relevance score for each document. This relevance score determines the
order of results on the output page.
Let me put together those new terms:
- several tens of terabytes of uncompressed data
- the inverted index resulting from this raw data
- dividing the index into pieces (index shards)
- each having a randomly chosen subset of documents from the full index
- A pool of machines serves requests for each shard
- several tens of terabytes of uncompressed data -> the inverted index - many terabytes of data
- index -> index shards
- parallelizable -> a randomly chosen subset of documents from the full index
The search process is challenging because of
the large amount of data: The raw documents
comprise several tens of terabytes of uncompressed data, and the inverted index resulting
from this raw data is itself many terabytes of
data. Fortunately, the search is highly parallelizable by dividing the index into pieces
(index shards), each having a randomly chosen
subset of documents from the full index. A
pool of machines serves requests for each shard,
and the overall index cluster contains one pool
for each shard. Each request chooses a machine
within a pool using an intermediate load balancer—in other words, each query goes to one
machine (or a subset of machines) assigned to
each shard. If a shard’s replica goes down, the
load balancer will avoid using it for queries,
and other components of our cluster-management system will try to revive it or eventually replace it with another machine. During
the downtime, the system capacity is reduced
in proportion to the total fraction of capacity
that this machine represented. However, service remains uninterrupted, and all parts of the
index remain available.
The final result of this first phase of query
execution is an ordered list of document identifiers (docids). As Figure 1 shows, the second phase involves taking this list of docids and
computing the actual title and uniform
resource locator of these documents, along
with a query-specific document summary.
Document servers (docservers) handle this
job, fetching each document from disk to
extract the title and the keyword-in-context
snippet. As with the index lookup phase, the
strategy is to partition the processing of all
documents by
- randomly distributing documents into
smaller shards
- having multiple server replicas responsible for handling each shard, and
- routing requests through a load balancer.
The docserver cluster must have access to
an online, low-latency copy of the entire Web.
In fact, because of the replication required for
performance and availability, Google stores
dozens of copies of the Web across its clusters.
In addition to the indexing and document serving phases, a GWS also initiates several
other ancillary tasks upon receiving a query,
such as sending the query to a spell-checking
system and to an ad-serving system to generate relevant advertisements (if any). When all
phases are complete, a GWS generates the
appropriate HTML for the output page and
returns it to the user’s browser.
Using replication for capacity and fault-tolerance
We have structured our system so that most
accesses to the index and other data structures
involved in answering a query are read-only:
Updates are relatively infrequent, and we can
often perform them safely by diverting queries
away from a service replica during an update.
This principle sidesteps many of the consistency issues that typically arise in using a general-purpose database.
We also aggressively exploit the very large
amounts of inherent parallelism in the application: For example, we transform the lookup
of matching documents in a large index into
many lookups for matching documents in a
set of smaller indices, followed by a relatively
inexpensive merging step. Similarly, we divide
the query stream into multiple streams, each
handled by a cluster. Adding machines to each
pool increases serving capacity, and adding
shards accommodates index growth. By parallelizing the search over many machines, we
reduce the average latency necessary to answer
a query, dividing the total computation across
more CPUs and disks. Because individual
shards don’t need to communicate with each
other, the resulting speedup is nearly linear.
In other words, the CPU speed of the individual index servers does not directly influence the search’s overall performance, because
we can increase the number of shards to
accommodate slower CPUs, and vice versa.
Consequently, our hardware selection process
focuses on machines that offer an excellent
request throughput for our application, rather
than machines that offer the highest single thread performance.
In summary, Google clusters follow three
key design principles:
- Software reliability. We eschew fault-tolerant hardware features such as redundant power supplies, a redundant array
of inexpensive disks (RAID), and high quality components, instead focusing on
tolerating failures in software.
- Use replication for better request throughput and availability. Because machines are
inherently unreliable, we replicate each
of our internal services across many
machines. Because we already replicate
services across multiple machines to
obtain sufficient capacity, this type of
fault tolerance almost comes for free.
- Price/performance beats peak performance.
We purchase the CPU generation that
currently gives the best performance per
unit price, not the CPUs that give the
best absolute performance.
- Using commodity PCs reduces the cost of
computation. As a result, we can afford to
use more computational resources per
query, employ more expensive techniques
in our ranking algorithm, or search a
larger index of documents.
Leveraging commodity parts
Google’s racks consist of 40 to 80 x86-based
servers mounted on either side of a custom
made rack (each side of the rack contains
twenty 20u or forty 1u servers). Our focus on
price/performance favors servers that resemble
mid-range desktop PCs in terms of their components, except for the choice of large disk drives. Several CPU generations are in active
service, ranging from single-processor 533-
MHz Intel-Celeron-based servers to dual 1.4-
GHz Intel Pentium III servers. Each server
contains one or more integrated drive electronics (IDE) drives, each holding 80 Gbytes.
Index servers typically have less disk space
than document servers because the former
have a more CPU-intensive workload. The
servers on each side of a rack interconnect via
a 100-Mbps Ethernet switch that has one or
two gigabit uplinks to a core gigabit switch
that connects all racks together.
Our ultimate selection criterion is cost per
query, expressed as the sum of capital expense
(with depreciation) and operating costs (hosting, system administration, and repairs) divided by performance. Realistically, a server will
not last beyond two or three years, because of
its disparity in performance when compared
to newer machines. Machines older than three
years are so much slower than current-generation machines that it is difficult to achieve
proper load distribution and configuration in
clusters containing both types. Given the relatively short amortization period, the equipment cost figures prominently in the overall
cost equation.
Because Google servers are custom made,
we’ll use pricing information for comparable
PC-based server racks for illustration. For
example, in late 2002 a rack of 88 dual-CPU
2-GHz Intel Xeon servers with 2 Gbytes of
RAM and an 80-Gbyte hard disk was offered
on RackSaver.com for around $278,000. This
figure translates into a monthly capital cost of
$7,700 per rack over three years. Personnel
and hosting costs are the remaining major
contributors to overall cost.
The relative importance of equipment cost
makes traditional server solutions less appealing for our problem because they increase performance but decrease the price/performance.
For example, four-processor motherboards are
expensive, and because our application parallelizes very well, such a motherboard doesn’t
recoup its additional cost with better performance. Similarly, although SCSI disks are
faster and more reliable, they typically cost
two or three times as much as an equal-capacity IDE drive.
The cost advantages of using inexpensive,
PC-based clusters over high-end multiprocessor servers can be quite substantial, at
least for a highly parallelizable application like
ours. The example $278,000 rack contains
176 2-GHz Xeon CPUs, 176 Gbytes of
RAM, and 7 Tbytes of disk space. In comparison, a typical x86-based server contains
eight 2-GHz Xeon CPUs, 64 Gbytes of RAM,
and 8 Tbytes of disk space; it costs about
$758,000. In other words, the multiprocessor server is about three times more expensive
but has 22 times fewer CPUs, three times less
RAM, and slightly more disk space. Much of
the cost difference derives from the much
higher interconnect bandwidth and reliability of a high-end server, but again, Google’s
highly redundant architecture does not rely
on either of these attributes.
Operating thousands of mid-range PCs
instead of a few high-end multiprocessor
servers incurs significant system administration and repair costs. However, for a relatively homogenous application like Google,
where most servers run one of very few applications, these costs are manageable. Assuming tools to install and upgrade software on
groups of machines are available, the time and
cost to maintain 1,000 servers isn’t much more
than the cost of maintaining 100 servers
because all machines have identical configurations. Similarly, the cost of monitoring a
cluster using a scalable application-monitoring system does not increase greatly with cluster size. Furthermore, we can keep repair costs
reasonably low by batching repairs and ensuring that we can easily swap out components
with the highest failure rates, such as disks and
power supplies.
The power problem
Even without special, high-density packaging, power consumption and cooling issues can
become challenging. A mid-range server with
dual 1.4-GHz Pentium III processors draws
about 90 W of DC power under load: roughly
55 W for the two CPUs, 10 W for a disk drive,
and 25 W to power DRAM and the motherboard. With a typical efficiency of about 75 percent for an ATX power supply, this translates
into 120 W of AC power per server, or roughly 10 kW per rack. A rack comfortably fits in
25 ft2 of space, resulting in a power density of
400 W/ft2
. With higher-end processors, the
power density of a rack can exceed 700 W/ft2
.
Unfortunately, the typical power density for
commercial data centers lies between 70 and
150 W/ft2
, much lower than that required for
PC clusters. As a result, even low-tech PC
clusters using relatively straightforward packaging need special cooling or additional space
to bring down power density to that which is
tolerable in typical data centers. Thus, packing even more servers into a rack could be of
limited practical use for large-scale deployment as long as such racks reside in standard
data centers. This situation leads to the question of whether it is possible to reduce the
power usage per server.
Reduced-power servers are attractive for
large-scale clusters, but you must keep some
caveats in mind. First, reduced power is desirable, but, for our application, it must come
without a corresponding performance penalty: What counts is watts per unit of performance, not watts alone. Second, the
lower-power server must not be considerably
more expensive, because the cost of depreciation typically outweighs the cost of power.
The earlier-mentioned 10 kW rack consumes
about 10 MW-h of power per month (including cooling overhead). Even at a generous 15
cents per kilowatt-hour (half for the actual
power, half to amortize uninterruptible power
supply [UPS] and power distribution equipment), power and cooling cost only $1,500
per month. Such a cost is small in comparison to the depreciation cost of $7,700 per
month. Thus, low-power servers must not be
more expensive than regular servers to have
an overall cost advantage in our setup.
Hardware-level application characteristics
Examining various architectural characteristics of our application helps illustrate which
hardware platforms will provide the best
price/performance for our query-serving system. We’ll concentrate on the characteristics of
the index server, the component of our infrastructure whose price/performance most heavily impacts overall price/performance. The main
activity in the index server consists of decoding
compressed information in the inverted index
and finding matches against a set of documents
that could satisfy a query. Table 1 shows some
basic instruction-level measurements of the
index server program running on a 1-GHz dual processor Pentium III system.
The application has a moderately high CPI,
considering that the Pentium III is capable of
issuing three instructions per cycle. We expect
such behavior, considering that the application traverses dynamic data structures and that
control flow is data dependent, creating a significant number of difficult-to-predict
branches. In fact, the same workload running
on the newer Pentium 4 processor exhibits
nearly twice the CPI and approximately the
same branch prediction performance, even
though the Pentium 4 can issue more instructions concurrently and has superior branch
prediction logic. In essence, there isn’t that
much exploitable instruction-level parallelism
(ILP) in the workload. Our measurements
suggest that the level of aggressive out-of-order, speculative execution present in modern processors is already beyond the point of
diminishing performance returns for such
programs.
A more profitable way to exploit parallelism
for applications such as the index server is to
leverage the trivially parallelizable computation. Processing each query shares mostly read only data with the rest of the system, and
constitutes a work unit that requires little communication. We already take advantage of that
at the cluster level by deploying large numbers
of inexpensive nodes, rather than fewer high end ones. Exploiting such abundant thread level parallelism at the microarchitecture level
appears equally promising. Both simultaneous
multithreading (SMT) and chip multiprocessor (CMP) architectures target thread-level
parallelism and should improve the performance of many of our servers. Some early experiments with a dual-context (SMT) Intel
Xeon processor show more than a 30 percent
performance improvement over a single-context setup. This speedup is at the upper bound
of improvements reported by Intel for their
SMT implementation.
We believe that the potential for CMP systems is even greater. CMP designs, such as
Hydra and Piranha, seem especially promising. In these designs, multiple (four to eight)
simpler, in-order, short-pipeline cores replace
a complex high-performance core. The penalties of in-order execution should be minor
given how little ILP our application yields,
and shorter pipelines would reduce or eliminate branch mispredict penalties. The available thread-level parallelism should allow
near-linear speedup with the number of cores,
and a shared L2 cache of reasonable size would
speed up interprocessor communication.
Memory system
Table 1 also outlines the main memory system performance parameters. We observe
good performance for the instruction cache
and instruction translation look-aside buffer,
a result of the relatively small inner-loop code
size. Index data blocks have no temporal locality, due to the sheer size of the index data and
the unpredictability in access patterns for the
index’s data block. However, accesses within
an index data block do benefit from spatial
locality, which hardware prefetching (or possibly larger cache lines) can exploit. The net
effect is good overall cache hit ratios, even for
relatively modest cache sizes.
Memory bandwidth does not appear to be
a bottleneck. We estimate the memory bus
utilization of a Pentium-class processor system to be well under 20 percent. This is mainly due to the amount of computation required
(on average) for every cache line of index data
brought into the processor caches, and to the
data-dependent nature of the data fetch
stream. In many ways, the index server’s memory system behavior resembles the behavior
reported for the Transaction Processing Performance Council’s benchmark D (TPC-D). For such workloads, a memory system with a
relatively modest sized L2 cache, short L2
cache and memory latencies, and longer (perhaps 128 byte) cache lines is likely to be the
most effective.
Large-scale multiprocessing
As mentioned earlier, our infrastructure
consists of a massively large cluster of inexpensive desktop-class machines, as opposed
to a smaller number of large-scale shared memory machines. Large shared-memory
machines are most useful when the computation-to-communication ratio is low; communication patterns or data partitioning are
dynamic or hard to predict; or when total cost
of ownership dwarfs hardware costs (due to
management overhead and software licensing
prices). In those situations they justify their
high price tags.
At Google, none of these requirements
apply, because we partition index data and
computation to minimize communication
and evenly balance the load across servers. We
also produce all our software in-house, and
minimize system management overhead
through extensive automation and monitoring, which makes hardware costs a significant
fraction of the total system operating expenses. Moreover, large-scale shared-memory
machines still do not handle individual hardware component or software failures gracefully, with most fault types causing a full
system crash. By deploying many small multiprocessors, we contain the effect of faults to
smaller pieces of the system. Overall, a cluster
solution fits the performance and availability
requirements of our service at significantly
lower costs.
At first sight, it might appear that there are
few applications that share Google’s characteristics, because there are few services that
require many thousands of servers and
petabytes of storage. However, many applications share the essential traits that allow for a
PC-based cluster architecture. As long as an
application orientation focuses on the
price/performance and can run on servers that
have no private state (so servers can be replicated), it might benefit from using a similar
architecture. Common examples include high volume Web servers or application servers that
are computationally intensive but essentially
stateless. All of these applications have plenty
of request-level parallelism, a characteristic
exploitable by running individual requests on
separate servers. In fact, larger Web sites
already commonly use such architectures.
At Google’s scale, some limits of massive
server parallelism do become apparent, such as
the limited cooling capacity of commercial
data centers and the less-than-optimal fit of
current CPUs for throughput-oriented applications. Nevertheless, using inexpensive PCs
to handle Google’s large-scale computations
has drastically increased the amount of computation we can afford to spend per query,
thus helping to improve the Internet search
experience of tens of millions of users.
