Context sensitive indexes for performance optimization of sql queries in multi-tenant-multi-application environments

Context-Sensitive Indexes in RDBMS for Performance Optimization
of SQL Queries in Multi-Tenant/Multi-Application Environments
Arjun K Sirohi1,2
1
PSR( Performance, Scalability, Reliability) Engineering, Oracle U.S.A. Inc, Bellevue, WA, U.S.A.
2
School of Information and Communication Technology, Gautam Buddha University, G Noida, India
arjun.sirohi@oracle.com
Keywords: Indexes, Mixed-Load Queries, Multi-tenant Queries, Multi-Application Queries, SQL Performance
Optimization.
Abstract: With the recent shift towards cloud-based applications and Software as a Service (SaaS) environments,
relational databases support multi-tenant and multi-application workloads that query the same set of data
stored in common tables, using SQL queries. These SQL queries have very different query constructs and
data-access requirements leading to different optimization needs. However, the business-users' expect sub-
second response times in getting the data that they requested. The current RDBMS architectures where
indexes “belong” to a table without any object privileges of their own, and, therefore, must be considered
and used by the optimizer for all SQLs referencing the table(s), pose multiple challenges for the optimizer
as well as application architects and performance tuning experts, especially as the number of such indexes
grows. In this paper, we make the case for “Context-Sensitive Indexes”, whereby applications and tenants
could define their own indexes on the shared, transactional database tables to optimize the execution of their
SQL queries, while at the same time having the optimizer keep such indexes isolated from other
applications and tenants/users for the purposes of query optimization.
1 INTRODUCTION
Today, there is an increasing need for relational
database management systems (RDBMS) to support
mixed-load and multi-tenant queries. Additionally,
the changing landscape of enterprise business
applications, coupled with the easy availability of
cloud services and software-as-a-service paradigm,
many small and medium sized businesses who could
not afford to implement applications like Customer
Relationship management (CRM) in-house, have
now started adopting these applications through
cloud providers as hosted application services. All
this is leading to a situation where the back-end
databases need to support multiple concurrent
applications that send SQL queries against the same
schema and data, albeit with very different query
constructs, data access requirements and
optimization goals. The providers of SaaS and Cloud
environments also face the challenge to support
multi-tenancy, wherein the applications’ schema in
the database may be shared and yet the data stored in
those schema tables is isolated and protected from
each other. What is not changing, though, is the
business-users' expectations of sub-second response
times in getting the data that they requested of the
database. Cloud and SaaS providers are finding
themselves implementing various solutions that can
act as a differentiator.
Most commercial enterprise business applications
use a relational database like Oracle at the back end
and all processing of data is managed through SQL
queries. At the lowest grain, the performance of such
SQL queries often dictates the performance and
scalability of the application, among other factors. In
this paper we examine the existing application and
database approach towards use of indexes and their
impacts on the performance of SQL queries. A large
number of performance problems arise from the fact
that most SQL queries are dynamically generated at
run-time by middleware SQL-generation engines
based on an abstract layer of the logical data model,
which results in complex queries. It has been

lamented that the use of indexes in the database to
help performance of SQL queries is as old as
relational databases and yet we find inadequacy and
poor quality of indexes (Leach and Lahdenmaki,
2005). So we feel that it is time to take a fresh look
at the humble indexes in the context of multi-tenant
and multi-application environments to see how
indexes can be better leveraged to improve query
performance.
This paper begins by providing the motivation
and background work done on this topic. Next, we
discuss the contributions of our paper followed by a
discussion of existing architectures and their
limitations as related to the topic of our research.
This includes highlighting the two most important
limitations in existing architectures. Next, we
discuss our proposal of context-sensitive indexes
and their implementation aspects, including
maintenance. The paper then presents results from
experiments conducted to support the proposal. The
paper concludes with ideas for taking action and
possible directions for implementation.
2 MOTIVATION AND
BACKGROUND WORK
Modern business applications have to process
increasing volumes of data stored in transactional
databases. This means that SQL queries have to
process more data but still achieve acceptable query
processing times. This has added increased pressure
on RDBMS providers to come up with new
techniques to handle increased volumes of data on
one hand and to keep query processing times within
acceptable limits, if not improve further, on the
other. Adding to this complexity are the Cloud and
SaaS offerings from independent service and
application providers. To support such application
models, database vendors have tried to make
changes and adjustments to the database
architectures. For example, the offerings and multi-
tenant scenarios from IBM, like separate databases
for tenants, shared database but different schemas or
shared database and shared schema for multiple
tenants have been discussed (Chong, 2012). There
are similar offerings made by Microsoft Corporation
(Chong, Carraro and Wolter, 2006). Oracle’s latest
database release 12c also provides many
enhancements to support multi-tenancy (Oracle,
2014). All such offerings and architectures are
focused around the use and sharing of database
instances, databases, schemas and applications. Most
research has been focused on optimizing such
architectures in order to achieve user satisfaction in
terms of query processing times.
Improving query processing time is an ongoing,
complex research subject and advancements have
been made in recent years by RDBMS vendors in
many different areas of query optimization. One
such area of advancement is the use of various types
of indexes used in query optimization. We find that
most of the existing work done by researchers on
indexes has been mostly on the aspects of internal
implementation of various types of indexing
mechanisms. For example, the issues related to
optimizing of multidimensional index trees for main
memory access were researched and addressed by
using two-dimensional CR-tree index structure that
performs searches much faster than the ordinary R-
tree and at the same time consuming less memory
space (Kihong and Sang, 2001). These researchers
have focused on the physical design structures of
indexes and better algorithms to optimize storage
and access. The impacts of multiple and different
types of applications like OLTP applications and
transactional business intelligence applications
sending a mixed and varied query load to the
database, on the performance and scalability of
queries has not been the focus of much research. The
origins of mixed query workloads like OLTP and
analytics workloads and their characteristics have
been researched and discussed earlier (Powley,
Martin and Bird, 2008). However, much of the
research has been directed at finding ways and
means to somehow shield the transactional business
applications’ queries from the effects of analytical
and business intelligence type of queries. For
example, in one solution, it was proposed to adjust
the memory allocation in order to meet response
time goals for mixed workloads (Brown, Mehta,
Carey, and Livny, 1994). Another solution proposed
was an algorithm for memory allocation and
prioritization based on resource usage, workload
characteristics and performance statistics (Pang,
Carey and Livny, 1995). Other researchers put their
focus on creating benchmark standards that could
effectively bench such mixed-load applications
(Krueger, Tinnefeld, Grund, Zeier, and Plattner,
2010). To the best of our knowledge, there is no
current published research on query optimization
through creation and use of context-sensitive
indexes, whereby tenants/users/schemas and
applications could decide which indexes should be
used as input by the optimizer to arrive at the best
access paths for their SQL queries instead of the

optimizer being burdened with this task of
evaluating all available indexes on a given table.
3 CONTRIBUTIONS OF THIS
PAPER
In this paper, we present a novel way to optimize
query processing for the shared environments where
a single shared-database, shared-schema approach
provides multi-tenancy or multi-applications
environments or both. In these approaches, multiple
instances of an application and/or multiple different
applications point to the shared database and shared
schema tables that hold the applications’ data. The
tenants are identified by a unique column in every
table that stores the application data, most
commonly named “Tenant_Id”. While this shared
approach provides considerable cost savings in
terms of infrastructure and operational costs, there
are performance and scalability considerations and
concerns arising out of this architecture. Similarly,
even in single-tenant database environments, there
are often multiple applications and modules within
applications with different optimization goals and
SQL query constructs that are a cause for
performance concerns.
In this paper, we propose the creation and
handling of indexes in a manner that can lead to
better optimized query processing, thereby resulting
in better performance and scalability of such multi-
tenant and/or multi-applications environments. We
focus on improving the performance of SQL queries
in a mixed-load and/or multi-tenant enterprise
business application through the creation and use of
context-sensitive indexes, owned by an application
and/or tenant/user, shared with other
applications/tenants/users if needed, and used by the
RDBMS optimizer solely for SQL queries
originating from these owner applications’ modules
and actions and/or tenants/users.
For the purposes of this paper, we refer to the
Oracle database’s implementation of schema and
indexes in order to compare with our proposed
solution since Oracle is the most widely used
commercial database. Also, in this paper, we use the
term “context-sensitive indexes” to describe indexes
that are only visible to and/or owned by specific
applications, schemas and/or tenants and used by the
RDBMS optimizer ONLY to optimize access paths
for queries originated by the specific
tenant/user/schema and/or applications’ modules and
sub-modules termed as Actions. These have the
potential to significantly improve the performance of
the SQL queries in a shared, multi-tenant and/or
multi-application architecture and as a result
improve the performance and scalability of such
applications.
A word on the limits of the scope of our research
and this paper’s proposal is in order here. We want
to clarify that we do not delve into the physical
implementation characteristics and optimizations of
table and index structures. We also do not propose
any new type of index structures. What we have
researched and what we propose is a methodology
for the segregation of ownership of the tables that
contain application data from the creation and use of
context-sensitive indexes that support the search and
retrieval of data from such tables by multiple
tenants/users/schemas and/or applications’ Modules
and Actions.
4 EXISTING ARCHITECTURES
AND LIMITATIONS
In this section, we provide a snapshot of the existing
architectures and their limitations as related to the
topic of our research. For example, in Oracle
database, tables and indexes are schema objects that
exist in different namespaces. (Oracle Database SQL
Language Reference 12c, 2014). A query is defined
as an operation that retrieves data from one or more
tables or views. In this reference, a top-level
SELECT statement is called a query. Further, Oracle
documentation defines indexes as structures that
allow fast access path to data and are meant to
reduce disk I/O, thereby improving query
performance. Indexes are independent of the data in
the tables and can be created or dropped without
affecting the data. Similarly, SQL statements are
written independent of the indexes and are not
affected by any changes in indexes. (Oracle®
Database Concepts, 2014).
Indexes can be created in one’s own schema well
as in another schema, with appropriate privileges.
One key property of indexes in Oracle database that
affects query optimization is whether the index is
visible or invisible to the optimizer. An invisible
index is maintained by Data Manipulation Language
(DML) operations, but it is not used by the optimizer
during query hard parsing and optimization unless
explicitly asked for.
Similarly, in IBM’s DB2 UDB database, an
index is described as "a set of pointers that are
logically ordered by the values of one or more keys.

Indexes are used to improve performance and ensure
uniqueness" (IBM DB2, 2014). In the Hierarchy of
DB2 UDB authorities and privileges on database
objects like tables and indexes, the only privilege
that can be granted on indexes is "CONTROL",
which grants the privilege to drop the index
(Wasserman, 2012). DB2 UDB documentation
further states that “Although the query optimizer
decides whether to use a relational index to access
relational table data, it is up to you to decide which
indexes might improve performance and to create
those indexes” (IBM DB2, 2014).
The optimizer is described as built-in database
software that determines the most efficient way to
execute a SQL statement by considering factors
related to the objects referenced and the conditions
specified in the statement. The Oracle database
optimizer receives the parsed query and generates a
set of potential plans for the SQL statement based on
available access paths and hints. It estimates the cost
of each plan based on statistics in the data
dictionary. Optimizer statistics are created for the
purposes of query optimization and are stored in the
data dictionary. The cost of plans is an estimated
value proportional to the expected resource use
needed to execute the statement with a particular
plan. It compares the costs of plans and chooses the
lowest-cost plan, known as the query plan. As
documented, “To choose an access path, the
optimizer first determines which access paths are
available by examining the conditions in the
statement's WHERE clause and it’s FROM clause.
The optimizer then generates a set of possible
execution plans using available access paths and
estimates the cost of each plan, using the statistics
for the index, columns, and tables accessible to the
statement. Finally, the optimizer chooses the
execution plan with the lowest estimated cost”
(Oracle® Database Performance Tuning Guide,
2014).
In the existing architecture, we note that the
RDBMS optimizer is burdened with all the analysis
and decision-making that goes into finding the most
optimal index access paths for the data being sought
by an SQL query. The popular RDBMS
implementations like Oracle, DB2 UDB and SQL
Server provide limited control to the applications
and developers, by way of SQL constructs, using
index hints or altering session properties that can
influence the optimizer’s access path selection in
using specific indexes built on tables. During its
query plan generation, the optimizer takes into
consideration all indexes created on tables with few
exceptions like cases where in Oracle an index has
been made unusable or invisible. We find that one
limitation in the existing architecture is that during
this process, the optimizer does not consider the
already available contextual metadata in terms of
index ownership and the application’s module/action
from which the SQL originated. The documented
reason for this is that “some schema objects, such as
clusters, indexes, triggers, and database links, do not
have associated object privileges” (Oracle®
Database Security Guide, 2014)
This is best illustrated with examples. Let’s say
in Oracle database, a user/schema ‘B’ representing a
specialized application, has read access and index
creation privileges on table ‘T1’ which is owned by
another user/schema ‘A’. Now indexes can be
created on table ‘T1’ and owned by user/schema ‘B’
such as ‘B.T1-IDX1’. Suppose that user/schema ‘A’
has two other existing indexes on table ‘T1’, namely
‘A.T1-IDX2’ and ‘A.T1-IDX3’. Now, when a SQL
from user/schema ‘A’ comes to the optimizer that
involves data access from table ‘T1’, the optimizer
takes into consideration all three indexes - ‘A.T1-
IDX2’, ‘A.T1-IDX3’ as well as ‘B.T1-IDX1’. It
does not matter that index ‘B.T-IDX1’ was created
by application designers to only support specific
SQLs coming from user/schema ‘B’ for a specific
application’s module and action. Under this current
architecture where indexes “belong” to a table
without any object privileges of their own, and,
therefore, must be considered and used by the
optimizer for all SQLs referencing the table, we find
two performance related problems as described in
the following sections.
4.1 Higher Hard Parse times
The first problem in the existing architecture where
indexes “belong” to tables and thus all indexes on all
tables referenced in the query must be evaluated by
the optimizer during hard parse, is that this can often
lead to higher hard parse times and locking
contentions, especially when multiple hard parse
requests come in to the database concurrently
accessing the same objects. In most N-Tier modern
business applications, the SQLs are dynamically
generated by middleware SQL-generation engines
based on an abstracted logical data model layer. As a
result of and due to various limitations in the process
of run-time dynamic SQL generation, the number of
tables referenced in SQLs has been getting larger. In
addition, the number of indexes created on each
table has also increased significantly due to the
number and type of applications that need to be
supported. For example, in the latest Oracle Fusion

Applications, we frequently find SQLs referencing
20 to 40 tables, each with a large number of indexes.
The final result is that the number of indexes for all
tables in a SQL that need to be considered and
evaluated by the optimizer has increased manifold.
In addition, the optimizer goes through more
permutations at hard parse time to get the most
optimal execution plan due to many more query
transformations in newer releases of RDBMSs like
Oracle, DB2 UDB and SQL Server. In cases where
there is a cost tie between different plans, the
optimizer needs to decide which of the competing
indexes to use. For example, Oracle database
optimizer sorts index names alphabetically as the
method of choosing one index over another, when
both have the same cost. This results in increased
query hard parse time.
4.2 Potential for Sub-Optimal Plans
The second problem relates to sub-optimal execution
plans chosen by the optimizer for some SQL queries,
in part due to the large number of, and sometimes,
competing indexes. The problem itself emanates
from the combined effects of the complexity and
size of the dynamically generated SQLs, the
increased number of tables referenced in these
SQLs, the large number of indexes created on these
tables, the increased complexity and newer features
of the optimizer code itself. The net result is that the
optimizer has to now evaluate many more potential
access paths and combinations of optimizations to
satisfy the data requested by SQL queries. This
results in a higher probability of the optimizer
making some sub-optimal choices, which can be
very detrimental to an application’s performance and
scalability. The contribution of the large number of
indexes to this problem of sub-optimal plan choices
is quite significant and thus, cannot be ignored.
Oracle documents this problem in Oracle® Database
Performance Tuning Guide under Section 14.1.1
titled ‘Tuning the Logical Structure’ as follows:
“Note that creating an index to tune one statement
can affect the optimizer's choice of execution plans
for other statements. For example, if you create an
index to be used by one statement, then the
optimizer can choose to use that index for other
statements in the application as well. For this reason,
re-examine the application's performance and
execution plans, and rerun the SQL trace facility
after you have tuned those statements that you
initially identified for tuning” (Oracle® Database
Performance Tuning Guide, 2014). We note that this
is a very limiting factor that creates additional
testing and analysis workload for application
architects and performance tuning experts when they
want to create a new index for a specific
application’s module and/or action as noted in the
documentation above.
We also note that in databases like Oracle, a lot
of useful information about a SQL query is captured
for reporting and performance analysis purposes but
is currently not utilized by the optimizer as
additional inputs into the metadata and statistics on
which it relies to generate efficient query plans. For
example, applications typically pass certain
parameters along with the SQL request to the
database including MODULE and ACTION using
the package
DBMS_APPLICATION_INFO.SET_MODULE.
However, this information is not currently utilized
by the optimizer for the purposes of optimizing
query plans, though it is used for certain other
functions including Enterprise Manager’s
performance graphs, Active Session History (ASH)
and Automatic Workload Repository (AWR)
reports.
In brief, when run-time, dynamically generated
SQLs from multiple applications’ modules and
actions execute SQL queries against the same tables,
with very different SQL constructs and optimization
goals, we find that this current architecture puts
undue burden on the optimizer to make the access
plan choices that can sometimes result in sub-
optimal performance of such queries and resultant
poor application performance.
We would like to note one additional limitation
with the current architecture. Databases like Oracle
have traditionally provided ‘HINTS’ to be used in
SQLs as a way to influence the optimizer’s decision-
making and choices in the selection of access paths,
including indexes. The hints include ways to specify
that the optimizer should use named indexes as
provided in the hint section of a SQL query. As
documented, “The INDEX hint instructs the
optimizer to use an index scan for the specified
table. You can use the INDEX hint for function-
based, domain, B-tree, bitmap, and bitmap join
indexes.” (Oracle Database Online Documentation,
2014). While this approach worked for older
applications that used hand-crafted SQLs, the
approach has many serious pitfalls for the run-time,
dynamically generated SQLs that are produced by
SQL-generation engines based on an abstracted
logical data model, that are more the norm in N-tier
application architectures that we see in the industry
today. Adding hints in such dynamically generated

SQLs has been found to be very risky in terms of
unintended consequences for optimizer plan
selection and performance of SQL queries. For
example, in one implementation of Oracle Fusion
Applications, addition of hints in ADF View Objects
(VOs) by some developers and performance tuning
experts, to tune one set of queries, resulted in many
other queries originating from other modules/actions
to suddenly perform extremely poorly. Subsequent
performance analysis of such SQLs showed that the
added hints were the root cause of performance
degradation.
To the best of our knowledge and in our
research, we do not find any current implementation
of our proposed concept of “Context-Sensitive
Indexes”. We describe "Context-Sensitive Indexes"
as database index structures built on tables that are
used in a manner which provides methods for the
RDBMS optimizer to establish the context in terms
of Tenant/User/Schema as well as Applications'
MODULE and ACTION for the optimizer to use
such established context for minimizing the number
of indexes evaluated during hard parse with the aim
of achieving the most optimal access paths and
execution plan possible for the SQL query with
reduced hard parse.
5 CONTEXT-SENSITIVE
INDEXES FOR SQL QUERY
PERFORMANCE
There are many factors that affect the performance
of SQLs. Our focus in this paper is on making the
use of indexes context-sensitive by the optimizer to
arrive at the most optimal execution plan for a given
tenant/user/schema or application’s module/action.
The idea behind our proposal is two-fold. One,
reducing the hard parse time by limiting the number
of indexes that the optimizer needs to evaluate while
arriving at query plans. Two, minimizing the
possibility of the optimizer not choosing the most
appropriate index and thus using an inefficient
execution plan, by providing the optimizer a
restricted list of indexes based on the application
owner/architect’s knowledge of the application and
data model including data shape in tables.
Context for indexes can be defined in terms of any
one or more of the following:
1. Tenant context in case of multi-tenant
applications or Schema/User context in case of
multiple schemas/users accessing data from the
same tables in the database.
2. Application Module context in both cases of
single and multi-application environments.
3. Application Module’s Action context in case
of both single and multi-application module
environments.
For example, the new Fusion Applications from
Oracle comprise of a number of different
applications like CRM (Customer Relationship
Management), HCM (Human Capital Management),
CDM (Customer Data Management), OTBI (Oracle
Transactional Business Applications), and Business
Intelligence Applications (BI Apps) among others.
Within each of the applications, there are various
Modules, each with one or more Actions. As
mentioned earlier, the SQLs are dynamically
generated at run time, by ADF and BI Server.
Within these applications, a number of schema
tables are shared not only within applications’
modules but also across the applications. Even
though the tables are shared, each application has
some unique requirements that necessitate use of
specific indexes for SQLs originating from those
applications’ modules and actions. For example,
SQLs for the Fusion CRM Applications generated
for the transactional application and User Interface
(UI) can be quite different from the SQLs generated
by the Business Intelligence Server (BI Server) for
OTBI and BI Apps Extract, Transform, Load (ETL)
queries, which have entirely different SQL
constructs and optimization goals due to their very
nature. Even within the Fusion CRM applications,
modules that serve the UI use cases are very
different from uses cases like Microsoft Outlook
integration using web services. In one internal
implementation of Fusion Applications in Oracle,
there are over four hundred module-action
combinations. In a large number of such cases,
“context-sensitive indexes” will provide application
architects a very useful way of defining and creating
indexes to serve specific use cases without the fear
of interfering with the performance stability of
queries from other applications and modules. All
this becomes possible with our proposal to make
indexes context-sensitive. In the next few
paragraphs, we discuss how context for indexes is
important in different scenarios.

5.1 The Multi-Tenant/User/Schema
Context-Sensitive Indexes
Oracle 12c has introduced the concept of pluggable
databases (PDB), whereby one PDB could be used
for each tenant in the multi-tenant architecture
(Oracle Database Online Documentation, 2014).
However, for the use cases where multiple tenants
share the same database and schema, whether in
Oracle or other RDBMS systems, it has been a
challenge for application architects and developers
to achieve high performance. Our proposed concept
of context-sensitive indexes provides for allowing
the tenant/user/schema context in the creation and
use of indexes. In our proposed architecture, it
should be possible for RDBMSs to provide a
methodology to not only segregate the ownership of
specific indexes amongst tenants/users/schemas but
also partition/filter the common indexes based on
tenant id. This is possible if we move away from the
historical architecture of indexes belonging to a
table, rather than belonging to a tenant/user/schema
and then providing visibility of those partitions to
specific tenants/users/schemas whose data is indexed
in those partitions.
In the context of tenants/users/schemas, we propose
that the indexes (or index partitions) on common
tables should be created and owned by
tenants/users/schemas and the indexed data in such
indexes should only contain filtered data based on
tenant id.
Our proposed architecture is slightly different from
the existing concept of partitioned tables and
indexes. The existing global partitioned indexes in
databases provide the means to create indexes on
different partitioning keys and it is currently possible
to partition the tables and/or indexes by tenant key.
By creating such partitioned indexes, we can take
advantage of the optimizer’s ability to use partition-
pruning and technique known as partition-wise joins
to improve performance. However, this approach
requires the creation and maintenance of such
partitioned indexes for all tenants. This current
methodology works for use cases where the indexes
on the same columns are required for use by all
tenants. However, it does not work in use cases
where different tenants require indexes on different
sets of columns. For example, let us say there are
100 tenants and an index named Index1 on Colum1,
Column2 and Column3, partitioned by Tenant-ID. In
the current architecture, this index would result in
creation and maintenance of 100 partitions of
Index1. If this index were needed only by 10 tenants,
then the remaining 90 partitions will not only add to
the maintenance overhead but we also put these 90
tenants confronting the problems of higher hard
parse time and instable execution plans that we have
described earlier in the paper due to high number of
indexes. In our proposal, tenants/users/schemas
would have the flexibility to create indexes that they
need and have the index filtered down to only their
own data. Other tenants/users/schemas would
therefore not be burdened with such indexes that
they do not have a need for. Overall, the database
will also have lower cost of index maintenance by
restricting the number, size and partitions of indexes.
It may be argued that our proposed methodology
may lead to more number of indexes overall and
arguably slightly higher cost of maintenance.
However, this need not be the case if due diligence
is done by application architects in creating a
scheme of visible/invisible indexes from amongst
the existing indexes itself. Also, the performance
gains for queries from tenants/users will more than
offset the slightly increased maintenance costs, if
any. Such tenant specific filtered indexes (or index
partitions) will be smaller in size and possibly have
lower heights, with fewer levels and fewer leaf
blocks because these only contain index entries for a
specific tenant. Specifically, index fast full scan
operations will benefit because their performance is
directly proportional to the index size. There will be
lesser number of index splits due to inserts, updates
and deletes because now such operations will be
divided and spread across the many different
tenant/user indexes. DML operations like insert,
update and delete done by a specific tenant/user will
not require the updating of indexes for another
tenant/user, thereby isolating index maintenance
costs to the specific tenant.
Another key advantage in our proposed architecture
is that tenants/users will get the freedom to create
their own indexes that help with their specific types
of queries and data shapes without burdening the
optimizer from considering their indexes during
creation of query plans for other tenants/users. This
is very significant in terms of index creation and
maintenance and will provide further isolation
between tenants/users. The performance gains for
SQL queries will therefore come from savings in
hard parse time due to the fewer number of indexes
to be evaluated by the optimizer, from potentially
more stable, optimal execution plans as well as from
better execution times due to the smaller traversal

paths through such smaller and more compact
indexes. We establish the performance advantages of
our proposed architecture in the experimental results
presented in Section 6 of this paper.
5.2 Applications’ Module and Action
To recall, in the current RDBMS architectures,
indexes do not have object privileges and for the
purposes of the optimizer’s choice of SQL execution
plan, it must consider all available indexes for tables
referenced in the SQL query. Often, a large number
of indexes get created on a table to support many
different SQL queries to support multiple
applications, modules and sub-modules (Actions).
When this happens, the optimizer, in some cases, is
unable to choose the best possible query plan due to
the large number of competing indexes. We propose
that indexes should be treated as database objects on
which privileges can be granted and revoked. The
reason for this proposal is to give application
architects the ability to make the fine-grained
choices about which indexes they wish to have
access to for the purposes of optimization of their
applications’ SQL queries and which indexes to
ignore as irrelevant for the optimization of their SQL
queries. A common use case scenario these days is
the different types of applications accessing data in
the same tables, but with different SQL constructs
and optimization goals. For example, in Oracle’s
Fusion Applications, the needs of the transactional
Fusion Application’s queries are very different from
the requirements of OTBI queries. There are also
many additional types of applications like mobile
applications, bulk data loading, and BI Apps ETL
for pulling data to the data warehouse. By modifying
the RDBMS architecture to allow database objects
like indexes to have object privileges, we can
facilitate the creation of an application’s Module and
Action context which can then be used by the
optimizer to limit the indexes it considers for
optimization of SQL query plans.
The same concept can be further expanded to
different Modules and Actions within modules of an
application, whereby, it should be possible for
application architects to specify to the RDBMS
optimizer, which indexes should be considered while
parsing and optimizing SQL queries originating
from specific modules/actions of an application.
Our proposed solution improves upon and provides a
more generic and broader way to guide the optimizer
in choosing efficient query plans over the existing
methodology of specifying particular index usage
hints in the text of specific SQLs, which is becoming
very hard to achieve for dynamically generated
SQLs.
It can be argued that one down-side of this feature
could be generation of multiple child cursors for the
same SQL because of the context and possibly
resulting in increased number of hard parses.
However, the effects of this feature would be similar
to the effects of existing database features like
adaptive cursor sharing, which create new child
cursors based on factors like bind-peeking etc. Thus,
the benefits of reduced hard parse times and more
optimal execution plans should outweigh the effect
of some additional hard parsing and creation of child
cursors. Similar to any other database optimizer
feature like adaptive cursor sharing, this new feature
could also have a parameter to switch it on or off.
5.3 Implementation Aspects of
In the current RDBMS architectures, indexes do not
have object privileges. For the purposes of the
optimizer’s choice of SQL execution plan, it must
consider all available indexes for a given table
referenced in the SQL query. Our proposed
architecture can be achieved by RDBMS vendors in
multiple ways. The first and foremost requirement is
to upgrade the status of indexes in databases by
declaring indexes as database objects on which
VISIBLE privilege can be granted and revoked.
“Context-Sensitive Indexes” implementation will
need to be done at two levels of context as discussed
in subsequent sections.
5.3.1 Tenant/User/Schema Context
The first enhancement to implement “context-
sensitive indexes” would be to declare indexes as
database objects on which an object privilege,
VISIBLE, can be granted or revoked. This can be
implemented similar to currently used privileges on
objects like TABLE (SELECT, INSERT etc). By
granting VISIBLE privilege on specific indexes to
tenants/users/schemas, the optimizer can eliminate
from evaluation certain indexes during hard parse on
which the tenant/user/schema does not have
VISIBLE privilege. This will give
tenants/users/schemas the ability to make the fine-
grained choices about which indexes they wish to
have access to for the purposes of optimization of
their SQL queries and which indexes to ignore as

irrelevant. In addition, a Global Visible scheme for
indexes can help application architects to grant
VISIBLE to all Tenants/Users/Schemas for some or
all indexes as needed. Alternately, the default
privilege could be VISIBLE for all
tenants/users/schemas on all indexes, as in current
RDBMS implementations. Database
administrators/application architects could then
selectively revoke VISIBLE privilege on specific
indexes that they do not wish to be used by the
optimizer for their SQLs. This will achieve
tenant/user/schema context-sensitive indexes. For
example, say there are two users ‘A’ and ‘B’. The
following statements will grant and revoke VISIBLE
privilege:
GRANT VISIBLE ON <index_name> TO
<User B>;
REVOKE VISBILE ON <index_name>
FROM <User A>;
The VISBILE privilege’s grant and revoke
architecture can address the implementation of
context-sensitive indexes for multi-tenant and multi-
user/schema environments as depicted in Figure 1.
Declarative Scheme for User/Schema Context-Sensitive Indexes
Context/Index TABLE1.IDX1 TABLE1.IDX2 TABLE1.IDX3 TABLE1.IDX4 TABLE1.IDX5
Table Owner User/Schema Yes Yes Yes Yes Yes
User/Schema2 Yes Yes Yes No Yes
User/Schema3 Yes No No No No
User/Schema4 Yes Yes No No Yes
User/Schema5 Yes No Yes Yes Yes
User/Schema6 Yes No No Yes Yes
Figure 1: Declarative Scheme for Tenant/User/Schema
Context.
5.3.2 Applications’ Module and Action
Context
The aim is to make it possible for creating and
declaring indexes that should only be used by
specified applications’ modules and actions. Hence,
the visibility of such context-sensitive indexes will
need to be controlled by an additional, optional
scheme of granting or revoking visibility on indexes
to/from specific combinations of Applications,
Modules and Actions. This would require creation
and maintenance of metadata related to application
Modules and Actions by the RDBMS. For example,
new object types – MODULE and ACTION – can
be created that could be owned by
tenants/users/schemas and stored in dictionary
tables, for example, DBA_MODULES,
DBA_MODULE_ACTIONS. These modules and
actions could be granted VISIBLE privilege on
indexes. Further, metadata for index visibility
privileges could be stored in a new dictionary table,
for example, DBA_IDX_PRIVS, similar to existing
table privileges in DBA_TAB_PRIVS.
Application architects and owners can then
register/create application modules and actions
with/in the database in the same manner as other
objects are currently created. Once
created/registered, the application modules and
actions could be used for index visibility grants.
VISIBLE grant could be granted to an application’s
specific module and action once registered with the
database using CREATE MODULE and CREATE
ACTION statements. For example, user/schema ‘A’
could create/register MODULE1 and ACTION1,
ACTION2 under that module and then control the
visibility on indexes as under:
CREATE MODULE <Module1> FOR USER <User
A>;
CREATE ACTION <Action1> FOR MODULE
<User A>.<Module1>;
CREATE ACTION <Action2> FOR MODULE
<User A>.<Module1>;
GRANT VISIBLE ON <User A>.<Idx3> TO
<Module1.Action1>;
REVOKE VISIBLE ON <User A>.<Idx3> TO
<Module1.Action2>;
The above example would allow the index IDX3 to
be used by the optimizer for SQLs originating from
MODULE1.ACTION1 for user ‘A’ but not used for
SQLs originating from MODULE1.ACTION2 for
the same user. Providing such a granular, declarative
way for the implementation of context-sensitive
indexes to application architects and performance
tuning professionals, the RDBMS optimizer’s
burden of making the right index choices will be
reduced. As a result, the optimizer will do less work,
save on time and database resources and arguably
produce efficient, stable execution plans for SQL
queries. Based on testing and performance review,
applications’ architects, database administrators and
SQL tuning professionals will be able to make
adjustments to the context-sensitive indexes
declarative scheme to further fine-tune the matrix for
optimal performance

A simplified chart example of such a declarative
scheme of context-sensitive indexes is shown in
Figure 2.
Declarative Scheme for Index Visibility to Applications' Module-Action
Context/Index TABLE1.IDX1 TABLE1.IDX2 TABLE1.IDX3 TABLE1.IDX4
Application1.Module1 Yes Yes No No
Application1.Module2 Yes No Yes No
Application1.Module1.Action1 Yes Yes No Yes
Application1.Module1.Action2 Yes Yes Yes No
Application1.Module2.Action1 Yes No Yes Yes
Application2.Module1.Action1 Yes No No Yes
Application2.Module1.Action3 Yes No No Yes
Application2.Module2.Action1 Yes No No No
Figure 2: Declarative Scheme for Application
Module/Action Context-Sensitive Indexes.
Our other proposed methodology described above -
of making indexes as objects on which grants can be
given or revoked using tenants/user/schemas for the
purposes of granting privileges on indexes - would
co-exist with the applications’ module/action context
declarative scheme under our proposed granular
architecture. Such context-sensitive indexes will go
a long way in addressing the problems of
performance and execution plan stability for SQL
queries in a multi-tenant and/or multi-application
mixed-load environment.
5.3.3 Maintenance of Context-Sensitive
Indexes
It could be argued that the proposed context-
sensitive indexes may increase the maintenance
overhead. However, we feel that the overhead in
managing context-sensitive indexes is expected to be
quite insignificant, especially in the context of
packaged enterprise business applications which
have many applications/modules/actions and
users/schemas/tenant. The database optimizer,
though very sophisticated, does not have a better
understanding of the applications, their modules and
actions than the application and SQL-tuning
architects, especially when there are separate
architects dedicated to different applications,
modules and actions. Even under the present
arrangement, the database optimizer does not play
any significant role in the index design process. It is
the application and SQL-tuning architects who
design indexes and go through the SQL-tuning
process iteratively, to add, remove and modify
indexes. They do so, trying to find the elusive fine
balance of just the right indexes that can serve all
applications, modules and actions as well as
tenants/users/schemas. This is simply based on their
deep knowledge of the data model, application flows
and SQL queries for those applications. What we
have observed in the case of enterprise business
applications like Oracle Fusion Applications, is that
the design and development teams for different
applications and modules/actions often have to add
or modify indexes to tune the queries for their
respective applications. Based on the review and
analysis of hundreds of poorly performing SQL
queries in Oracle Fusion Cloud Applications, we
observe that very often, such actions to add or
modify indexes, inadvertently cause adverse effects
on the performance of many other applications'
queries, which does not come to be realized until
late in the release cycle, very often after customer
complaints of sudden performance degradation after
an upgrade or patch cycle. In addition, with the
current architecture, it is not only very expensive to
carry out meaningful regression testing for all
applications/modules/actions when one or more
indexes are added by developers for tuning one
particular application/module/action but is nearly
impractical to do so. The proposed context-sensitive
indexes will provide a simple methodology for
application and SQL-tuning architects to add,
modify or remove indexes for the purposes of the
optimizer's consideration while optimizing the
execution plans by simply making the indexes
visible or invisible. There is no additional overhead
than what application architects currently do except
deciding which indexes to use out of all the available
ones.
6 EXPERIMENTAL RESULTS
We sampled some SQLs from Oracle’s currently
deployed Fusion Applications and benchmarked
them to determine the efficacy of proposed context-
sensitive indexes. For the purpose of this
experiment, we used Oracle’s currently provided
methodology to make indexes visible or invisible.
The following commands were used:
ALTER INDEX <Index Name> INVISIBLE;
ALTER INDEX <Index1> VISIBLE;
6.1 Benchmark Results: Higher Hard
Parse times Use Case
We benchmarked representative SQLs from Oracle
Fusion CRM Application against Oracle 11.2.0.3
database to find the co-relation between the number
of visible indexes and the SQL hard parse time. One

such SQL had 34 tables referenced, which had a
combined total of 389 indexes. As the graph in
Figure 2 demonstrates, the hard parse time closely
followed the graph line for the number of visible
indexes. Similar results were recorded for many
SQLs benchmarked in a similar manner. What we
found is that the higher the number of visible
indexes, the higher the hard parse time.
Figure 2: Co-relation between number of visible indexes
and hard parse time.
6.2 Benchmark Results: Sub-Optimal
Plan Use Case
In the current Oracle Fusion Cloud Applications,
high hard parse time and sub-optimal execution
plans are very significant problems that cause poor
user experience. The optimizer not picking the right
indexes is a frequent finding in the slow SQL tuning
analysis. We benchmarked such a representative
SQL from Oracle Fusion CRM Application against
Oracle 11.2.0.3 database to highlight use cases
where the optimizer may choose the wrong indexes
resulting in sub-optimal execution plan and
extremely poor performance. The benchmarked SQL
references 27 tables which have a total of 272
indexes. As Figure 3 and 4 demonstrate,
performance in terms of execution time, CPU time
and logical I/Os (buffer gets) is dramatically better
in the use case where the number of visible indexes
was restricted.
and execution, CPU and I/O time.
and Buffer Gets and Disk Reads.
7 CONCLUSIONS
The use of indexes in various forms and physical
implementations has long been established as an
industry standard practice for RDBMSs and is used
in many types of applications including enterprise
business applications. However, indexes have
largely been treated as being tightly coupled with
tables for the purposes of privileges and visibility to
the optimizer for use in generation of query
execution plans. The effects of having a large
number of indexes on the optimizer’s performance
have also been much researched. However, treating
indexes as schema objects on which privileges and
grants can be given has not been the focus of much
research. In this paper we have researched,
documented and presented a novel architecture
whereby indexes can be made context-sensitive to
accommodate a declarative scheme of telling the
optimizer which indexes to consider for specific
tenants/users/schema and/or applications’ modules

and actions. The current methodology of the
optimizer taking into consideration all indexes
created on a table without regard to the context in
which a SQL query has been sent to the database,
poses performance problems and can be a risk to the
success of enterprise business applications. We have
proposed a solution that takes into account context
attributes for indexes under which the optimizer
should process SQL queries to improve the
performance of such SQLs, for which a US Patent
application has been filed (ORA150617-US-PSP).
Adoption of the proposed solution by RDBMS
vendors can thus provide significant performance
improvements in RDBMS optimizer query plan
generation, thereby improving the performance and
scalability of enterprise applications on one hand
and consuming fewer database resources on the
other.
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