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Renormalization of NoSQL Database Schemas: 37th International Conference, ER 2018, Xi'an, China, October 22–25, 2018, Proceedings



NoSQL applications often use denormalized databases in order to meet performance goals, but this introduces complications as the database itself has no understanding of application-level denormalization. In this paper, we describe a procedure for reconstructing a normalized conceptual model from a denormalized NoSQL database. The procedure’s input includes functional and inclusion dependencies, which may be mined from the NoSQL database. Exposing a conceptual model provides application developers with information that can be used to guide application and database evolution.
Renormalization of NoSQL Database Schemas
Michael J. Mior and Kenneth Salem
Cheriton School of Computer Science, University of Waterloo
Abstract. NoSQL applications often use denormalized databases in order to
meet performance goals, but this introduces complications as the database it-
self has no understanding of application-level denormalization. In this paper, we
describe a procedure for reconstructing a normalized conceptual model from a
denormalized NoSQL database. The procedure’s input includes functional and
inclusion dependencies, which may be mined from the NoSQL database. Expos-
ing a conceptual model provides application developers with information that can
be used to guide application and database evolution.
Keywords: Renormalization, NoSQL, Database design, Conceptual modeling
1 Introduction
NoSQL databases, such as Apache Cassandra, Apache HBase, and MongoDB, have
grown in popularity, despite their limitations. This is due in part to their performance
and scalability, and in part because they adopt a flexible approach to database schemas.
Because these systems do not provide high-level query languages, applications must de-
normalize and duplicate data across physical structures in the database to answer com-
plex queries. Unfortunately, the NoSQL database itself typically has no understanding
of this denormalization. Thus, it is necessary for applications to operate directly on
physical structures, coupling applications to a particular physical design.
Although NoSQL systems may not require applications to define rigid schemas, ap-
plication developers must still decide how to store information in the database. These
choices can have a significant impact on application performance as well as the read-
ability of application code [6]. For example, consider an application using HBase to
track requests made to an on-line service. The same requests may be stored in multiple
tables since the structures available determine which queries can be asked. The choice
of data representation depends on how the application expects to use the table, i.e., what
kinds of queries and updates it needs to perform. Since the NoSQL system itself is un-
aware of these application decisions, it can provide little to no help in understanding
what is being represented in the database.
Together, the lack of physical data independence and the need for workload-tuned,
denormalized database designs creates challenges for managing and understanding phys-
ical schemas, especially as applications evolve. In our on-line service example, request
information might be stored twice, once grouped and keyed by the customer that sub-
mitted the request, and a second time keyed by the request subject or the request time. If
the application updates a request, or changes the information it tracks for each request,
these changes should be reflected in both locations. Unless the application developer
The final authenticated version is available online at
Project ProjID
Fig. 1: Schema example after renormalization
maintains external documentation, the only knowledge of this denormalization is em-
bedded within the source code. We aim to surface this knowledge by generating a useful
conceptual model of the data.
We refer to this surfacing task as schema renormalization. This work addresses the
schema renormalization problem through the following technical contributions:
We present a semi-automatic technique for extracting a normalized conceptual
model from an existing denormalized NoSQL database. It produces a normalized
conceptual model for the database, such as the one shown in Figure 1.
We develop an normalization algorithm in Section 5 which forms the core of our
approach. This algorithm uses data dependencies to extract a conceptual model
from the NoSQL system’s physical structures. Our algorithm ensures that the re-
sulting model is free from redundancy implied by these dependencies. To the best
of our knowledge, this is the first normalization algorithm to produce a schema in
interaction-free inclusion dependency normal form [9].
Finally, Section 6, presents a case study which shows the full schema renormaliza-
tion process in action for a NoSQL application. We use this case study to highlight
both the advantages and the limitations of our approach to renormalization.
The conceptual data model that our algorithm produces can serve as a simple refer-
ence, or specification, of the information that has been denormalized across the workload-
tuned physical database structures. We view this model as a key component in a broader
methodology for schema management for NoSQL applications.
2 Renormalization Overview
We renormalize NoSQL databases using a three step process. The first step is to pro-
duce a generic physical schema describing the physical structures that are present in the
NoSQL database. We describe the generic physical model in more detail in Section 3,
and illustrate how it can be produced for different types of NoSQL systems. The second
step is to identify dependencies among the attributes of the generic model. The depen-
dencies, which we discuss in Section 4, can be provided by a user with understanding of
the NoSQL system’s application domain or automatically using existing mining tech-
niques. We provide a brief overview of these steps in the following sections. More detail
is available in an extended technical report [14].
The final step in the renormalization process is to normalize the generic physical
schema using the dependencies, resulting in a logical schema such as the one repre-
sented (as an ER diagram) in Figure 1. This step is automated, using the procedure
described in Section 5. Our algorithm ensures that redundancy in the physical schema
captured by the provided dependencies is removed.
3 The Generic Physical Schema
The first step in the renormalization process is to describe the NoSQL database using
a generic schema. The schemas we use are relational. Specifically, a generic physical
schema consists of a set of relation schemas. Each relation schema describes a physical
structure in the underlying NoSQL database (e.g., a document collection). A relation
schema consists of a unique relation name plus a set of attribute names. Attribute names
are unique within each relation schema.
If the NoSQL database includes a well-defined schema, then describing the physical
schema required for renormalization is a trivial task. The generic schema simply iden-
tifies the attributes that are present in the table, and gives names to both the attributes
and the table itself. For example, Cassandra stores table definitions which can directly
provide the generic schema.
In general, we anticipate that the definition of a generic physical schema for an
application will require user involvement. However, there are tools that may assist with
this process. For example, Izquierdo et al. [7] have proposed a method for extracting a
schema from JSON records in a document store, which could be applied to extract the
generic physical schema required for renormalization.
4 Dependency Input
The second step of the renormalization process is to identify dependencies among at-
tributes in the generic physical schema. Our algorithm uses two types of dependencies:
functional dependencies (FDs) and inclusion dependencies (INDs). These two forms of
dependencies are easy to express and are commonly used in database design [11].
For input to our algorithm, we require that all INDs are superkey-based. That is,
for an IND R(A)S(B),Bmust be a superkey of S. We do not believe that this
is a significant restriction since we intend for INDs to be used to indicate foreign key
relationships which exist in the denormalized data. Indeed, Mannila and R¨
a [11]
have previously argued that only key-based dependencies are relevant to logical design.
5 Normalization Algorithm
Levene and Vincent [9] define a normal form for database relations involving FDs and
INDs referred to as inclusion dependency normal form (IDNF). They have shown that
Data: A set of relations R, FDs F, and INDs I
Result: A normalized set of relations R000 and new dependencies F0and I+000
F0,I+ Expand(F,I);// Perform dependency inference
BCNFDecompose(R,F0,I+);// BCNF normalization
);// Remove attributes/relations
);// Break circular INDs
Fig. 2: Algorithm for normalization to interaction-free IDNF
normalizing according to IDNF removes redundancy from a database design implied
by the set of dependencies. However, one of the necessary conditions for this normal
form is that the set of INDs is non-circular. This excludes useful schemas which ex-
press constraints such as one-to-one foreign key integrity. For example, for the relations
R(A,B)and S(B,C)we can think of the circular INDs R(A)=S(B)as expressing
a one-to-one foreign key between R(A)and S(B).
Levene and Vincent also propose an extension to IDNF, termed interaction-free
inclusion dependency normal form which allows such circularities. The goal of our
normalization algorithm is to produce a schema that is in interaction-free IDNF. This
normal form avoids redundancy implied by FDs and INDs while still allowing the ex-
pression of useful information such as foreign keys. As we show in Section 6, this
produces useful logical models for a real-world example.
Figure 2 provides an overview of our normalization algorithm, which consists of
four stages. In the reminder of this section, we discuss the normalization algorithm
in more detail. We will make use of a running example based on the simple generic
(denormalized) physical schema and dependencies shown in Figure 3.
5.1 Dependency Inference
To minimize the effort required to provide input needed to create a useful normalized
schema, we aim to infer dependencies whenever possible. Armstrong [1] provides a
well-known set of axioms which can be used to infer FDs from those provided as input.
Similarly, Mitchell [15] presents a similar set of inference rules for INDs.
Mitchell further presents a set of inference rules for joint application to a set of FDs
and INDs. We adopt Mitchell’s rules to infer new FDs for INDs and vice versa. The
pullback rule enables new FDs to be inferred from FDs and INDs. The collection rule
allows the inference of new INDs. These new dependencies allow the elimination of
attributes and relations via the Fold algorithm (see Section 5.3) to reduce the size of
the resulting schema while maintaining the same semantic information.
There is no complete axiomatization for FDs and INDs taken together [3]. Our
Expand procedure, which uses Mitchell’s pullback and collection rules for inference
from FDs and INDs, is sound but incomplete. However, it does terminate, since the
universe of dependencies is finite and the inference process is purely additive. Although
Physical Schema
Functional Dependencies
EmpProjects :EmpID !EmpName Employees :EmpID !EmpName,DeptID
EmpProjects :ProjID !ProjName Employees :DeptID !DeptName
Managers :DeptID !EmpID
Inclusion Dependencies
EmpProjects (EmpID,EmpName)Employees (...)
Employees (DeptID)Managers (...)
Managers (EmpID)Employees (...)
When attributes have the same names, we use ...on the right.
Fig. 3: Example generic physical schema and dependencies.
Expand may fail to infer some dependencies that are implied by the given set of FDs
and INDs, it is nonetheless able to infer dependencies that are useful for schema design.
5.2 BCNF Decomposition
The second step, BCNFDecompose, is to perform a lossless join BCNF decomposition
of the physical schema using the expanded set of FDs. When relations are decomposed,
we project the FDs and INDs from the original relation to each of the relations resulting
from decomposition. In addition, we add new INDs which represent the correspondence
of attributes between the decomposed relations. For example, when performing the de-
composition R(ABC )!R0(AB),R
00 (BC)we also add the INDs R0(B)R00 (B)
and R00 (B)R0(B). In our running example, we are left with the relations and de-
pendencies shown in Figure 4 after the Expand and BCNFDecompose steps.
5.3 Folding
Casanova and de Sa term the technique of removing redundant relations folding [2].
A complete description of our algorithm, Fold, is given in an extended technical re-
port [14]. Fold identifies attributes or relations which are recoverable from other rela-
tions. Specifically, folding removes attributes which can be recovered by joining with
another relation and relations which are redundant because they are simply a projection
of other relations. Fold also identifies opportunities for merging relations sharing a
common key.
Consider the EmpProjects0relation which contains the EmpName attribute. Since
we have the IND EmpProjects’(EmpID,EmpName) Employees(...)and
Physical Schema
Employees EmpID,EmpName,DeptIDDepartments DeptID,DeptName
EmpProjects EmpID,ProjIDEmpProjects0EmpID,EmpName
Managers DeptID,EmpIDProjects ProjID,ProjName
Functional Dependencies
Employees :EmpID !EmpName,DeptID Departments :DeptID !DeptName
Projects :ProjID !ProjName Managers :DeptID !EmpID
EmpProjects0:EmpID !EmpName
Inclusion Dependencies
Projects (ProjID)=EmpProjects (...)EmpProjects (EmpID)Employees (...)
EmpProjects0(EmpID)=EmpProjects (...)Managers (DeptID)Departments (...)
EmpProjects0(EmpID,EmpName)Employees (...)Managers Employees (...)
Fig. 4: Relations and dependencies after BCNF decomposition. Note that =is used to
represent bidirectional inclusion dependencies.
the FD Employees: EmpID !EmpName we can infer that the EmpName at-
tribute in EmpProjects0is redundant since it can be recovered by joining with the
Employees relation.
5.4 Breaking IND Cycles
Interaction-free IDNF requires that the final schema be free of circular INDs. Mannila
and R¨
a [11] use a technique, which we call BreakCycles, to break circular INDs
when performing logical database design. We adopt this technique to break IND cycles
which are not proper circular.
5.5 IDNF
The goal of our normalization algorithm is to produce a schema that is in interaction-
free IDNF with respect to the given dependencies. The following conditions are suf-
ficient to ensure that a set of relations Ris in interaction-free IDNF with respect to
a set of FDs Fand INDs I: (1) Ris in BCNF [5] with respect to F, (2) all the INDs
in Iare key-based or proper circular, and (3) Fand Ido not interact. A set of INDs
is proper circular if for each circular inclusion dependency over a unique set of re-
lations R1(X1)R2(Y2),R
m(Xm)R1(Y1), we have
Xi=Yifor all i. The schema produced by the normalization algorithm of Figure 2 is
in interaction-free IDNF. We provide a proof in an extended technical report [14].
6 RUBiS Case Study
In previous work, we developed a tool called NoSE [13], which performs automated
schema design for NoSQL systems. We used NoSE to generate two Cassandra schemas,
each optimized for a different workload (a full description is given in an extended tech-
nical report [14]). In each case, NoSE starts with a conceptual model of the database
which includes six types of entities (e.g., users, and items) and relationships between
them. The two physical designs consist of 9 and 14 Cassandra column families.
Our case study uses NoSE’s denormalized schemas as input to our algorithm so
that we can compare the schemas that it produces with the original conceptual model.
For each physical schema, we tested our algorithm with two sets of dependencies: one
manually generated from the physical schema, and a second mined from an instance of
that schema using techniques discussed in an extended technical report [14]. The first
set of dependencies resulted in a conceptual model that was identical (aside from names
of relations and attributes) to the original conceptual model used by NoSE, as desired.
For the second set of tests, renormalization produced the original model for the
smaller Cassandra schema. The mining process identified 61 FDs and 314 INDs. Rank-
ing heuristics were critical to this success. Without them, spurious dependencies lead to
undesirable entities in the output schema. For the larger schema, mining found 86 FDs
and 600 INDs, many of them spurious, resulting in a model different from the original.
No set of heuristics will be successful in all cases and this is an area for future work.
These examples show that FDs and INDs are able to drive meaningful denormaliza-
tion. Runtime for the normalization step of our algorithm was less than one second on
a modest desktop workstation in all cases.
7 Related Work
Much of the existing work in normalization revolves around eliminating redundancy in
relational tables based on different forms of dependencies. However, it does not deal
with the case where applications duplicate data across relations. Inclusion dependen-
cies are a natural way to express this duplication. Other researchers have established
normal forms using inclusion dependencies [11, 10, 9] in addition to FDs. Our approach
borrows from Manilla and R¨
a, who present a variant of a normal form involving in-
clusion dependencies and an interactive normalization algorithm. However, it does not
produce useful schemas in the presence of heavily denormalized data. Specifically, their
approach is not able to eliminate all data duplicated in different relations.
A related set of work exists in database reverse engineering (DBRE). The goal of
DBRE is to produce an understanding of the semantics of a database instance, com-
monly through the construction of a higher level model of the data. Unlike our work,
many approaches [4, 17] present only an informal process and not a specific algorithm.
There is significant existing work in automatically mining both functional [12] and
inclusion [8] dependencies from both database instances and queries. These approaches
complement our techniques since we can provide the mined dependencies as input into
our algorithm. Papenbrock and Naumann [16] present of heuristics for making use of
mined dependencies to normalize a schema according to BCNF. We leverage these to
incorporate mining into our algorithm as discussed in an extended technical report [14].
8 Conclusions and Future Work
We have developed a methodology for transforming a denormalized physical schema in
a NoSQL datastore into a normalized logical schema. Our method makes use of func-
tional and inclusion dependencies to remove redundancies commonly found in NoSQL
database designs. We further showed how we can make use of dependencies which were
mined from a database instance to reduce the input required from users. Our method has
a variety of applications, such as enabling query execution against the logical schema
and guiding schema evolution as application requirements change.
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... We have studied the evolution of the schema of two open-source projects using Apache Cassandra to store their data [6]: Minds 1 and PowSybl 2 . Minds is a social network with 35 schema versions. ...
... PowSybl is a framework for real and simulated power systems which also has 35 schema versions. In previous work [6], we focused on issues caused by an incorrect evolution of the database due to lack of consideration of additional information contained in the conceptual model, such as where the data are duplicated. One of the identified changes that is prone to cause these issues is the addition of a new column. ...
... Stage 1 aims to maintain inter-model consistency after a change to the schema by performing the required updates to the conceptual model to reflect the change in the schema. We propose using existing work that studies the renormalization of the schema in a normalized conceptual model [6,7], which generate a normalized conceptual model based on a column-oriented schema. We use past work focused on column-oriented databases [6] as a guideline for determining the specific changes to be performed in the conceptual model for each change in the schema. ...
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