rfc2258.txt
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RFC 2258 Internet Nomenclator Project January 1998
The distributed catalog service is logically one network service, but
it can be divided into pieces that are distributed and/or replicated.
Query resolvers access this distributed, replicated service using the
same techniques that work for multiple data repositories.
A Nomenclator system naturally includes many query resolvers.
Resolvers are independent, but renewable, query agents that can be as
powerful as the resources available at the user site. Caching
decreases the dependence of the resolver on the distributed catalog
service for frequently used meta-data, and on data repositories for
frequently used data. Caching thus improves the number of users that
can be supported and the local availability of the query service.
2.2 Meta-Data Techniques
The active catalog structures the information space into a collection
of relations about people, hosts, organizations, services and other
objects. It collects meta-data for each relation and structures it
into "access functions" for locating and retrieving data. Access
functions respond to the question: "Where is data to answer this
query?" There are two types of responses corresponding to the two
types of access functions. The first type of response is: "Look over
there." "Catalog functions" return this response; they constrain the
query search by limiting the data repositories contacted to those
having data relevant to the query. Catalog functions return a
referral to data access functions that will answer the query or to
additional catalog functions to contact for more detailed
information. The second response to "Where?" is: "Here it is!" "Data
access functions" return this response; they understand how to obtain
query answers from specific data repositories. They return tuples
that answer the query. Nomenclator supplies access functions for
common name services, such as the CCSO service, and organizations can
write and supply access functions for data in their repositories.
Access functions are implemented as remote or local services. Remote
access functions are services that are available through a standard
remote procedure call interface. Local access functions are
functions that are supplied with the query resolver. Local access
functions can be applied to a variety of indexing and data retrieval
tasks by loading them with meta-data stored in distributed catalog
service. Remote access functions are preferred over local ones when
the resources of the query resolver are inadequate to support the
access function. The owners of data may also choose to supply remote
access functions for privacy reasons if their access functions use
proprietary information or algorithms. Local functions are preferred
whenever possible, because they are highly replicated in resolver
caches. They can reduce system and network load by bringing the
resources of the active catalog directly to the users.
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Remote access functions are simple to add to Nomenclator and local
access functions are simple to apply to new data repositories,
because the active catalog provides "referrals" that describe the
conditions for using access functions. For simplicity, this document
describes referral techniques for exact matching of query strings.
Extensions to these techniques in Nomenclator support matching query
strings that contain wildcards or word-based matching of query
strings in the style of the CCSO services.
Each referral contains a template and a list of references to access
functions. The template is a conjunctive selection predicate that
describes the scope of the access functions. Conjunctive queries
that are within the scope of the template can be answered with the
referral. When a template contains a wildcard value ("*") for an
attribute, the attribute must be present in any queries that are
processed by the referral. The system follows the following rule:
Query Coverage Rule:
If the set of tuples satisfying the selection predicate in a query
is covered by (is a subset of) the set of tuples satisfying the
template, then the query can be answered by the access functions in
the reference list of the referral.
For example, the query below:
select * from People where country = "US" and surname = "Ordille";
is covered by the following templates in Lines (1) through (3), but
not by the templates in Lines (4) and (5):
(1) country = "US" and surname = "*"
(2) country = "US" and surname = "Ordille"
(3) country = "US"
(4) organization = "*"
(5) country = "US" and surname = "Elliott"
Referrals form a generalization/specialization graph for a relation
called a "referral graph." Referral graphs are a conceptual tool
that guides the integration of different catalog functions into our
system and that supplies a basis for catalog function construction
and query processing. A "referral graph" is a partial ordering of
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the referrals for a relation. It is constructed using the
subset/superset relationship: "S is a subset of G." A referral S is
a subset of referral G if the set of queries covered by the template
of S is a subset of the set of queries covered by the template of G.
S is considered a more specific referral than G; G is considered a
more general referral than S. For example, the subset relationship
exists between the pairs of referrals with the templates listed
below:
(1) country = "US" and surname = "Ordille"
is a subset of
country = "US"
(2) country = "US" and surname = "Ordille"
is a subset of
country = "US" and surname = "*"
(3) country = "US" and surname = "*"
is a subset of
country ="US"
(4) country = "US"
is a subset
"empty template"
but it does not exist between the pairs of referrals with the
following templates:
(5) country = "US"
is not a subset of
department = "CS"
(6) country = "US" and name = "Ordille"
is not a subset of
country = "US" and name = "Elliott"
In Lines (1) and (2), the more general referral covers more queries,
because it covers queries that list different values for surname. In
Line (3), the more general referral covers more queries, because it
covers queries that do not constrain surname to a value. In Line
(4), the specific referral covers only those queries that constrain
the country to "US" while the empty template covers all queries.
During query processing, wildcards in a template are replaced with
the value of the corresponding attribute in the query. For any query
covered by two referrals S and G such that S is a subset of G, the
set of tuples satisfying the template in S is covered by the set of
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tuples satisfying the template in G. S is used to process the query,
because it provides the more constrained (and faster) search space.
The referral S has a more constrained logical search space than G,
because the set of tuples in the scope of S is no larger, and often
smaller, than the set in the scope of G. Moreover, S has a more
constrained physical search space than G, because the data
repositories that must contacted for answers to S must also be
contacted for answers to G, but additional data repositories may need
to be contacted to answer G.
In constraining a query, a catalog function always produces a
referral that is more specific than the referral containing the
catalog function. Wildcards ("*") in a template indicate which
attribute values are used by the associated catalog function to
generate a more specific referral. In other words, catalog functions
always follow the rule:
Catalog Function Constrained Search Rule:
Given a referral R with a template t and a catalog function cf,
and a query q covered by t, the result of using cf to process q,
cf(q), is a referral R' with template t' such that q is covered
by t' and R' is more specific than R.
Catalog functions make it possible to import a portion of the indices
for the information space into the query resolver. Since they
generate referrals, the resolver can cache the most useful referrals
for a relation and call the catalog function as needed to generate
new referrals.
The resolver query processing algorithm obtains an initial set of
referrals from the distributed catalog service. It then navigates
the referral graph, calling catalog functions as necessary to obtain
additional referrals that narrow the search space. Sometimes, two
referrals that cover the query have the relationship of general to
specific to each other. The resolver eliminates unnecessary access
function processing by using only the most specific referral along
each path of the referral graph.
The search space for the query is initially set to all the data
repositories in the relation. As the resolver obtains referrals to
sets of relevant data repositories (and their associated data access
functions) it forms the intersection of the referrals to constrain
the search space further. The intersection of the referrals includes
only those data repositories listed in all the referrals.
Intersection combines independent paths through the referral graph to
derive benefit from indices on different attributes.
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2.3 Meta-Data and Data Caching
A Nomenclator query resolver caches the meta-data that result from
calling catalog functions. It also caches the responses for queries.
If the predicate of a new query is covered by the predicate of a
previous query, Nomenclator calculates the response for the new query
from the cached response of the old query. Nomenclator timestamps
its cache entries to provide measures of the currentness of query
responses and selective cache refresh. The timestamps are used to
calculate a t-bound on query responses [5][1]. A t-bound is the time
after which changes may have occurred to the data that are not
reflected in the query response. It is the time of the oldest cache
entry used to calculate the response. Nomenclator returns a t-bound
with each query response. Users can request more current data by
asking for responses that are more recent than this t-bound. Making
such a request flushes older items from the cache if more recent
items are available. Query resolvers calculate a minimum t-bound
that is some refresh interval earlier than the current time.
Resolvers keep themselves current by replacing items in the cache
that are earlier than the minimum t-bound.
2.4 Scale and Performance
Three performance studies of active catalog and meta-data caching
techniques are available [5]. The first study shows that the active
catalog and meta-data caching can constrain the search effectively in
a real environment, the X.500 name space. The second study examined
the performance of an active catalog and meta-data caching for single
users on a local area network. The experiments showed that the
techniques to eliminate data repositories from the search space can
dramatically improve response time. Response times improve, because
latency is reduced. The reduction of latency in communications and
processing is critical to large-scale descriptive query optimization.
The experiments also showed that an active catalog is the most
significant contributor to better response time in a system with low
load, and that meta-data caching functions to reduce the load on the
system. The third study used an analytical model to evaluate the
performance and scaling of these techniques for a large Internet
environment. It showed that meta-data caching plays an essential
role in scaling the distributed catalog service to millions of users.
It also showed that constraining the search space with an active
catalog contributes significantly to scaling data repositories to
millions of users. Replication and data caching also contribute to
the scale of the system in a large Internet environment.
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