Query Optimization for Big Data Analytics

International Journal of Computer Science & Information Technology (IJCSIT) Vol 11, No 5, October 2019
DOI: 10.5121/ijcsit.2019.11506 77
QUERY OPTIMIZATION FOR BIG DATA ANALYTICS
Manoj Muniswamaiah, Tilak Agerwala and Charles Tappert
Seidenberg School of CSIS, Pace University, White Plains, New York
ABSTRACT
Organizations adopt different databases for big data which is huge in volume and have different data
models. Querying big data is challenging yet crucial for any business. The data warehouses traditionally
built with On-line Transaction Processing (OLTP) centric technologies must be modernized to scale to the
ever-growing demand of data. With rapid change in requirements it is important to have near real time
response from the big data gathered so that business decisions needed to address new challenges can be
made in a timely manner. The main focus of our research is to improve the performance of query execution
for big data.
KEYWORDS
Databases, Big data, Optimization, Analytical Query, Data Analysts and Data Scientists.
1. INTRODUCTION
Big data analytics is a process of gathering and analyzing data which is immense in volume,
variety and velocity for making informed business decisions and take appropriate actions. There
are different databases with varied data models to store and query big data: Columnar databases
are used for read heavy analytical queries; online transactional processing databases are used for
quicker writes and consistency; NoSQL data stores provide high read and write scalability. They
are used in parallel processing of large volume of data and for horizontal distributed scaling;
HTAP is a single database which can perform both OLTP and OLAP by utilizing in-memory
computation. It is a hybrid of both OLTP and columnar database which can also be used for
machine learning applications [1].
There are various data stores that are designed and developed to handle specific needs of big data
and for optimizing performance. Relational databases can store and process structured data
efficiently but its lacks scalability, elasticity needed to handle the inflow of big data. Its
performance also decreases with read heavy queries. Similarly, columnar databases facilitate
faster reads. This makes it a better choice for analytical query processing. NoSQL data stores
provide horizontal scaling which makes it easier to add more resources on demand and thus are
specialized to handle unstructured data. The processed data is then stored in different databases to
be used by analysts for their business needs [1].
Performance optimization and different data models are important for data intensive applications.
The main challenge is to build data pipelines that are scalable, interactive, efficient and fault
tolerant. Data engineers optimize and maintain these data pipelines to ensure smooth data flow
which are crucial for the performance of the applications. Data engineers primary job is to
partition, normalize, index the base tables in order to improve the performance of the queries
which are used for dashboards and reports. These optimized copies would be stored in different
databases based on their data models for querying and quicker response. In this research we want
to automate this process where if data scientists issues a query against any desired database the

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query framework would be able to detect the optimized copies created and stored by data
engineers and execute the query against optimized copy rather than the base table which would
improve the performance of the query response.
2. BACKGROUND
There are several techniques to improve query performance and response time like partitioning
the base table, indexing on required columns, materialization and creating OLAP cubes. Indexing
helps in faster reads and retrieval of data. It is similar to data dictionary lookup, B-tree indexing
keeps the data sorted and allows for sequential access of the data [2]. Consistency and freshness
of the optimized copies is maintained by updating them whenever the base table changes.
BigDAWG, is a polystore framework implemented to support multiple databases compactible
with different data models. The main feature of BigDAWG is to provide location independence
which routes the queries to the desired databases and semantic completeness which lets the
queries to make use of the native database features effectively. Shims translate incoming queries
into their respective native database queries to take the advantages of the database features. One
of the important features provided by BigDAWG is casting where data from one island is been
converted into a model suitable for another island to enable inter-island queries and faster data
migration between their underlying databases. The optimizer component parses the request,
checks for syntax validation and creates a query plan to be executed by the engines. The monitor
component uses statistics gathered from the previous queries to pick up the best query plan for
execution. The migrator component is responsible for the movement of data between the data
engines [3].
Apache Kylin is an open source distributed online analytic processing engine developed by eBay
inc. It is built to support both ROLAP and MOLAP analytics. It provides SQL interface and sub-
second query latency for large datasets which boosts its query performance. OLAP cubes are built
offline using map reduce process. It reads data from source table and later uses map reduce to
build cuboids of all combinations at each level. The cubes are generally stored in columnar
database for faster reads. There are multiple cubing algorithms used for implementation. Recently
Apache Spark is been used to speed up the cube build process which is stored in HBase data
store. When users issue a query they are routed to be executed against the prebuilt cubes for
quicker response, if the desired cube does not exists then the query would be executed against the
Hadoop data [4].
Myria is an academia analytical engine been developed and provided as a cloud service. MyriaX
is a share-nothing relational analytical query execution engine which efficiently executes the
queries. It supports federated analytics and alleviates big data analysis. MyriaL is the query
language supported by Myria for querying [5]
Apache Hive supports adhoc-queries and is used for batch processing of big data. It converts
SQL-like queries in to map reduce jobs and executes the queries. HiveQL is the query language
used for processing of the data [6].
Kodiak is an analytical distributed data platform which uses materialized views to process
analytical queries. Various levels of materialized views are created and stored over petabytes of
data. Kodiak platform can maintain these views efficiently and update them automatically. In
Kodiak the query execution was three times faster and also used less resources [7].
Apache Lens is another open source framework which tries to provide a unified analytical layer

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of Hadoop and databases ecosystem using a REST server and query language CubeQL to store
and process the data cubes [8].
Analytical queries whose aggregates have been stored as OLAP cubes are used in reports and
dashboards. These cubes often need to be updated with latest aggregates. Multiple databases are
used within an organization for various tasks to store and retrieve the data. Data engineers
implement data pipelines to optimize datasets which would be later used by data analysts and data
scientists for research. Creating optimized copies involves partitioning and indexing of the base
tables. These optimized copies would later be stored in different databases for querying.
Our research focus and solution is to implement a query framework that routes the data analysts
and data scientists queries to the optimized copies created by data engineers which are stored,
maintained, updated automatically to achieve better query performance and reduce response time.
3. QUERY OPTIMIZER
Apache calcite framework is an open source database querying framework which uses relational
algebra for query processing. It parses the incoming coming query and converts them to logical
plans and later various transformations would be applied to convert this logical plan into an
optimized plan which has low cost in execution. This optimized logical plan would be converted
into physical plan to be executed against the databases by using traits. Query optimizer eliminates
logical plans which increase cost of the query execution based on cost model been defined.
Apache Calcite Schema contains details about the data formats present in the model which is used
by schema factory to create schema [9].
The query optimizer applies the planner rules to the relational node and generates different plans
with reduced cost by retaining the original semantics of the query. When a rule matches a pattern
query optimizer executes the transformations by substituting the subtree into the relation
expression and also preserves the semantics of it. These transformations are specific to each
database. The metadata component provides the query optimizer with details of the overall cost of
the execution of the relational expression and also the degree of parallelism that can be achieved
during execution [9].
The query optimizer uses cost-based dynamic programming algorithm which fires rules in order
to reduce the cost of the relational expression. This process continues until the cost of the
relational expression is not improved subsequently. The query optimizer takes into consideration
CPU cycles, memory been used and IO resource utilization cost to execute the relational
expression [9].
One of the techniques which is used to improve query processing is to use the optimized copies
been created by data engineers. The query optimizer needs to have the ability to make use of
these optimized copies to rewrite the incoming queries. Optimizer does this by substituting part of
the relational expression tree with optimized copies which it uses to execute the query and return
the response.
Algorithm
Optimization of relational expression R:
1. Register the relational expression R.
a. Check for the existence of appropriate optimized copy which can be used to substitute

80
the relational expression R.
b. If the optimized copy exists, then register the new relational expression R1 of the
optimized copy.
c. Trigger the transformation rules on relational expression R1 for cost optimization.
d. Obtain the best relational expression R1 based on cost and execute it against the
database.
2. If the relational expression R cannot be substituted with an existing optimized copy
a. Trigger the transformation rules on relational expression R.
b. Obtain the best relational expression based on cost and execute it against the
database[10].
4. ARCHITECTURE
Figure 1. Architecture of the SQL Framework
The analytical queries been executed would be parsed and different logical plans would be
generated. The query parser determines if the parsed relational expression can be substituted with
the registered optimized copies been created by data engineers automatically. It executes various
rules to obtain relational expression with minimal cost. The query would then be rewritten to be
executed against the optimized copy and the results would be returned [10].
SQL-Tool: Includes any tool which can be integrated using JDBC connection and execute SQL
analytical queries.
Query Engine: Apache Calcite open source framework that includes query optimizer been
extended to include optimized copies.
Metadata: Contains information about the schema and the features of the native databases.
Optimized copy: Optimized tables created by the data engineers.
RDBMS: Includes any relational database to store structured data.
HTAP: Hybrid database which has the scalability of NoSQL data stores.

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5. EVALUATION
Analytical queries help in data driven decision making process. In this paper we used NYC Taxi
and Limousine Commission (TLC) datasets provided under the authorization of Taxicab &
Livery Passenger Enhancement Programs consisting of the green and yellow taxi data [11].
The following experimental setup was made to benchmark and evaluate the query optimizer
performance to find the optimized copy of the data and substitute it in the query plan during run
time. Data engineers usually store these optimized copies in the columnar database for faster read
access. The tables and data used for the query evaluation was obtained from NYC taxi dataset
[11]. Data was stored in Mysql [12], Splice Machine [13] and Vertica database [14].
Evaluation was obtained based on the following setup
a.) Base tables had rows ranging from ~10,000,000 to ~20,000,000
b.) Optimized copy tables had rows ranging from ~1,000 to ~4,000,000
c.) Mysql, Splice Machine and Vertica database was running on single node instance with
Intel Quad Core Xeon 3.33GHz, 24 GB RAM and 1TB HDD.
d.) Base table data was stored in HDFS [15].
e.) Optimized copies were stored in Vertica database.
f.) SQL query optimizer used cost based dynamic algorithm to substitute optimized copy in
the query plan.
Figure 2. Query Response Time
The two bar graphs show the analytical queries been executed by the query optimizer against the
base table and the optimized copy. The query optimizer was successfully able to substitute the
optimized copy in the query plan when it existed and improved the performance of the query and
its response time.
6. CONCLUSION
In this research we were able to make extensions to cost based query optimizer to make use of the
optimized copies to obtain an improved query response time and performance. Various analytical
queries were executed to measure the results obtained. The query optimizer was successfully able
to substitute the optimized copies when existed during the runtime and modify the incoming
query to execute against it. Part of the future work is to extend this query optimizer to the cloud
databases.

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REFERENCES
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