Technical white paper Trafodion - Home - Esgyn€¦ · Technical white paper | Trafodion 2 Trafodion Apache Trafodion is an open source initiative to deliver an enterprise class SQL-on-HBase

Technical white paper

Trafodion ENTERPRISE CLASS TRANSACTIONAL SQL-ON-HBASE DBMS

Table of contents

Introducing Trafodion ................................................................................................................................................................... 2

Trafodion overview ................................................................................................................................................................... 2

Targeted Hadoop workload profile ........................................................................................................................................... 2

Transactional SQL application characteristics and challenges............................................................................................ 3

Trafodion innovations built upon Hadoop software stack ................................................................................................... 3

Leveraging HBase for performance, scalability, and availability ........................................................................................ 4

Trafodion innovation – value add improvements over vanilla HBase ............................................................................... 4

Salting of row keys ................................................................................................................................................................... 5

Trafodion feature overview......................................................................................................................................................... 5

Full-functioned ANSI SQL language support ........................................................................................................................... 5

Trafodion software architecture overview .............................................................................................................................. 6

Integrating with native Hive and HBase data stores ............................................................................................................. 6

Trafodion process overview and SQL execution flow ........................................................................................................... 6

Trafodion’s optimizer technology ............................................................................................................................................. 7

Extensible optimizer technology ........................................................................................................................................... 7

Optimized execution plans based on statistics .................................................................................................................. 7

Trafodion’s data flow SQL executor technology with optimized DOP............................................................................... 8

Trafodion optimizations for transactional SQL workloads .................................................................................................. 8

Trafodion innovation - Distributed Transaction Management .......................................................................................... 10

High availability and data integrity features.......................................................................................................................... 10

Summary of Trafodion benefits ............................................................................................................................................... 10

Where to go for more information .......................................................................................................................................... 12

Version 1

Technical white paper | Trafodion

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Trafodion

Apache Trafodion is an open source initiative to deliver an enterprise class SQL-

on-HBase DBMS engine that specifically targets transactional protected

operational workloads. Trafodion represents the combination of HBase and

transactional SQL technologies that have been developed leveraging more than

20 years of investments into database technology and solutions.

Introducing Trafodion

Trafodion is an open source initiative to develop an enterprise class SQL-on-HBase DBMS engine that specifically targets big

data transactional or operational workloads as opposed to analytic workloads. Transactional SQL encompasses workloads

previously described as OLTP (online transaction processing) workloads which were generated in support of traditional

enterprise-level transactional applications (ERP, CRM, etc.) and enterprise business processes. Additionally, transactions

have evolved to include social and mobile data interactions and observations using a mixture of structured and semi-

structured data.

Trafodion overview

• Comprehensive and full-functioned SQL DBMS which allows companies to reuse and leverage existing SQL skills to

improve developer productivity.

• Extends Hadoop HBase by adding support for ACID (atomic, consistent, isolated and durable) transaction protection that

guarantees data consistency across multiple rows, tables, SQL statements.

• Includes many optimizations for low-latency read and write transactions in support of the fast response time

requirements of the transactional SQL workloads.

• Hosted applications can seamlessly access and join data from Trafodion, native HBase, and Hive tables without expensive

replication or data movement overhead.

• Provides interoperability with new or existing applications and 3rd party tools via support for standard ODBC and JDBC

access.

• Designed to seamlessly fit within the existing IT infrastructure with no vendor lock-in by remaining neutral to the

underlying Linux and Hadoop distributions.

Targeted Hadoop workload profile

Hadoop workloads can be broadly categorized into 4 different workload types as shown in Figure 1 i.e. Operational,

Interactive, Non-Interactive, and Batch. These categories vary greatly in terms of their response time expectations as well as

the amount of data that is typically processed. The rightmost 3 categories are where the marketplace (vendors and

customers) have predominantly focused their attention and therefore these are the most mature in nature in terms of

development efforts and solution offerings. For the most part these categories represent efforts centered around

“analytics” and business intelligence processing on “big data” problems. These workloads are well positioned to leverage

Hadoop strengths and capabilities, map-reduce in particular.

In contrast, the leftmost workload defined as “Operational” is an emerging Hadoop market category and therefore the least

mature in nature. In part, this is a direct result of Hadoop being perceived as having a number of weaknesses (or gaps) in

terms of addressing the requirements for transactional SQL workloads. Traditionally these workloads have been relegated

to the domain of relational databases but there is growing interest and pressure to embrace these workloads in Hadoop due

to Hadoop’ s perceived benefits of significantly reduced costs, reduced vendor lock-in, and its ability to seamlessly scale to

larger workloads and data.

This is exactly the workload that Trafodion is targeting. Let’s next look at the characteristics and requirements of this

workload to better understand Hadoop’ s gaps and weaknesses and to better understand how Trafodion will address these.


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Figure 1. Hadoop Workload Profiles

Transactional SQL application characteristics and challenges

Transactional protected operational workloads are typically deemed mission critical in nature because they help companies

make money, touch their customers or prospects, or help them run and operate their business. Typically they have very

stringent requirements in terms of response times (sub-second) expectations, transactional data integrity, number of users,

concurrency, availability, and data volumes. With the advent of the “growing internet of things”, the number and types of

access devices has driven tremendous transaction and data growth and also changes in the type of data that needs to be

captured and utilized as part of these transactions. These next generation operational applications often require multi-

structured data types which implies that operational data is evolving rapidly to include a variety of data formats and types of

data, for example transactional structured data combined with visual images.

Combined, these requirements can expose Hadoop limitations in terms of transaction support, bulletproof data integrity,

real time performance, operational query optimization, and managing workloads comprised of a complex mix of

concurrently executing transactions all with varying priorities. Trafodion addresses each of these limitations and as a result

provides a differentiated DBMS capable of hosting these applications and their data.

Trafodion innovations built upon Hadoop software stack

Trafodion is designed to build upon and leverage Apache Hadoop and HBase core modules. Operational applications using

Trafodion transparently gain Hadoop’s advantages of affordable performance, scalability, elasticity, availability, etc. Figure 2

depicts a subset of the Hadoop software stack and those items colored in orange are specifically leveraged by Trafodion,

namely HBase, HDFS, and Zookeeper. To this stack, Trafodion adds (items colored in green) ODBC/JDBC drivers, the

Trafodion database software, and a new HBase distributed transaction management (DTM) subsystem for distributed

transaction protection across multiple HBase regions.

Trafodion interfaces to Hadoop services using their standard APIs. By maintaining API compatibility, Trafodion becomes

Hadoop distribution neutral thereby eliminating vendor lock-in by offering customers a choice of distributions to choose

from.

Trafodion is initially targeted to deliver innovation on top of Hadoop in these key areas:

• A full-featured ANSI SQL implementation whose database services are accessible via a standard ODBC/JDBC connection

• Provides a SQL relational schema abstraction which makes Trafodion look and feel like any other relational database

• Distributed ACID transaction protection

• Performant response times for transactions comprised of both reads and writes

• Parallel optimizations for both transactional and operational reporting workloads


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Figure 2. Trafodion and Hadoop Ecosystem

Leveraging HBase for performance, scalability, and availability

As stated previously, Trafodion is able to leverage all of the features and thereby all the advantages attributed to HBase

including parallel performance, virtually unlimited scalability, elasticity, and availability/disaster recovery protection.

These features are key to supporting operational workloads in production. For example:

• Fine grained load balancing, scalability, and parallel performance is provided via standard HBase services such as

autosharding Trafodion table data across multiple regions and region servers.

• Data availability and recovery in the event a server or disk fails or is decommissioned is provided by standard Hadoop and

HBase services such a replication and snapshots.

Additionally Trafodion is able to transparently leverage Hadoop distribution (e.g. Cloudera, Hortonworks) specific features

and capabilities since it accesses these distribution services via native HBase API’s. Powerful features such as compression

or encryption can be supplied “under the covers” for Trafodion defined tables as a result. Next let’s look at how Trafodion

brings innovation and value add to vanilla HBase.

Trafodion innovation – value add improvements over vanilla HBase

Although Trafodion stores its database objects in HBase/HDFS storage structures, it differs and brings value-add over

vanilla HBase in a multitude of ways as described below:

• Trafodion provides a relational schema abstraction on top of HBase which allows customers to leverage known and well

tested relational design methodologies and SQL programming skills.

• From a physical layout perspective, Trafodion uses standard HBase storage mechanisms (column family store using key-

value pairs) to store and access objects. Trafodion currently stores all columns in a single column family to improve

access efficiency and speed for operational data. Additionally Trafodion incorporates a column name encoding

mechanism to save space on disk and to reduce messaging overhead for the purposes of improving SQL performance.

• Unlike vanilla HBase that treats stored data as an uninterpreted array of bytes, Trafodion defined columns are assigned

specific data types that are enforced by Trafodion when inserting or updating its data contents. This not only greatly

improves data quality/integrity, it also eliminates the need to develop application logic to parse and interpret the data

contents.

• Vanilla HBase provides ACID transaction protection only at the row level. Trafodion extends ACID protection to application

defined transactions that can span multiple SQL statements, multiple tables, and rows. This greatly improves database

integrity by protecting the database against partially completed transactions i.e. ensuring that either the whole

transaction is completely materialized in the database or none of it.

• HBase’s native API is at a very low level and is not a commonly used programming API. In contrast, Trafodion’s API is ANSI

SQL which is a familiar and well known programming interface and allows companies to leverage existing SQL knowledge

and skills.

• Unlike HBase’s key structure that is comprised of a single uninterpreted array of bytes, Trafodion supports the common

relational practice of allowing the primary key to be a composite key comprised of multiple columns.


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• Finally unlike vanilla HBase, Trafodion supports the creation of secondary indexes that can be used to speed transaction

performance when accessing row data by a column value that is not the row key.

Salting of row keys

One known problematic area for HBase is supporting transactional workloads where data is inserted into a table in row key

order. When this happens, all of the I/O gets concentrated to a single HBase region which in turn creates a server and disk

hotspot and performance bottleneck. To alleviate this problem, Trafodion provides an innovative feature called “salting the

row key”.

To enable this feature the DBA specifies the number of partitions (i.e. regions) the table is to be split over when creating the

table e.g. “SALT USING 4 PARTITIONS”. Trafodion creates the table pre-split with one region per salt value. An internal hash

value column, “_SALT_”, is added as a prefix to the row key. Salting is handled automatically by Trafodion and is transparent

to application written SQL statements. As data is inserted into the table, Trafodion computes the salt value and directs the

insert to the appropriate region. Likewise, Trafodion calculates the salt value when data is retrieved from the table and

automatically generates predicates where feasible. MDAM technology (which is described in more detail in the section

entitled “Trafodion optimizations for transactional SQL workloads”) makes this process especially efficient. This is a very

lightweight operation with little overhead or impact to direct key access operations.

The benefits of salting are that you get more even data distributions across regions and improved performance via hotspot

elimination.

In summary, Trafodion incorporates a number of enhancements over vanilla HBase for the purposes of improving

transaction performance, data integrity, and DBA/developer productivity (i.e. by reducing application complexity through the

use of standard and well known relational practices and APIs).

Trafodion feature overview

Let’s now look at a high level overview of the Trafodion features. A more detailed drill down of each of these features is

provided in the sections below.

Trafodion includes:

• An enterprise-class SQL DBMS that provides all of the features you would expect from one of the merchant relational

database products that are on the market. The difference is that Trafodion leverages Hadoop services i.e. HBase/HDFS

for data storage.

• Full-functioned ANSI SQL language support including data definition, data manipulation, transaction control, and

database utilities.

• Linux and Windows ODBC/JDBC drivers.

• Distributed transaction management protection.

• Many SQL optimizations designed to improve operational workload performance.

All while retaining and extending expected Hadoop benefits! Now let’s dive into more details on these features.

Full-functioned ANSI SQL language support

Unlike most (if not all) NOSQL and other SQL-on-Hadoop products, Trafodion provides comprehensive ANSI SQL language

support including full-functioned data definition (DDL), data manipulation (DML), transaction control (TCL) and database

utility support.

• Unlike vanilla HBase, Trafodion provides support for creating and managing traditional relational database objects

including tables, views, secondary indexes, and constraints. Columns (table attributes) are assigned trafodion enforced

data types including numeric, character, varchar, date, time, interval, etc. Internationalization (I18N) support is provided

via Unicode encoding including UTF-8, UCS2, and ISO 8859-1 for both user data as well as the database metadata.

Comparisons and data manipulation between differing data encodings is transparently handled via implicit casting and

translation support.

• Trafodion provides comprehensive and standard SQL data manipulation support including SELECT, INSERT, UPDATE,

DELETE, and UPSERT/MERGE syntax with language options including join variants, unions, where predicates,

aggregations (group by and having), sort ordering, sampling, correlated and nested sub-queries, cursors, and many SQL

functions.


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• Utilities are provided for updating table statistics used by the optimizer for costing (i.e. selectivity/cardinality estimates)

plan alternatives, for displaying the chosen SQL execution plan, plan shaping, and a command line utility for interfacing

with the database engine.

• Explicit control statements are provided to allow applications to define transaction boundaries and to abort transactions

when warranted.

• Trafodion will support ANSI’s grant/revoke semantics to define user privileges in terms of managing and accessing the

database objects.

Trafodion software architecture overview

The Trafodion software architecture consists of 3 distinct layers: the client layer; the SQL database services layer; and the

storage engine layer (see Figure 3).

Figure 3. Trafodion's 3-layer software architecture

The first layer is the Client Services layer where

the operational application resides. The

operational application can be either customer

written or enabled via a 3rd party ISV

tool/application. Access to the Trafodion

database services layer is completed via a

standard ODBC/JDBC interface using a

Trafodion supplied Windows or Linux client

driver. Both type 2 and type 4 JDBC drivers are

supported and the choice is dependent on the

application requirements for response times,

number of connections, security, and other

factors.

The second layer is the SQL layer which consists

of the all the Trafodion database services. This

layer encapsulates all of the services required for managing Trafodion database objects as well as efficiently executing

submitted SQL database requests. Services include connection management, SQL statement compilation and optimized

execution plan creation, SQL execution (both parallel and non parallel) against Trafodion database objects, transaction

management, and workload management. Trafodion provides transparent parallel SQL execution as warranted thereby

eliminating the need for complex map-reduce programming development.

The third layer is the Storage Engine layer which consists of standard Hadoop services that are leveraged by Trafodion

including HBase, HDFS, and Zookeeper. Trafodion database objects are stored into native Hadoop (HBase/HDFS) database

structures. Trafodion handles the mapping of SQL requests into native HBase calls transparently on behalf of the

operational application. Trafodion provides a relational schema abstraction on top of HBase. In this way traditional

relational database objects (tables, views, secondary indexes) are supported using familiar DDL/DML semantics including

object naming, column definition and data types support, etc.

Integrating with native Hive and HBase data stores

One of the more powerful capabilities of Trafodion is its extensibility to also support and access data stored in native Hive or

HBase tables (non-Trafodion tables) using their native storage engines and data formats. The benefits that can be realized

include:

• Ability to run queries against native HBase or Hive tables without needing to copy them into a Trafodion table structure

• Optimized access to HBase and Hive tables without complex map-reduce programming

• Data can be joined across disparate data sources (e.g. Trafodion, Hive, HBase)

• Ability to leverage HBase’s inherent schema flexibility capabilities

Trafodion process overview and SQL execution flow

The Trafodion SQL Layer is comprised of a number of services or processes used for the purposes of handling connection

requests and SQL execution.


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• The process flow begins with the operational application or 3rd party client tool. The Windows or Linux client accesses the

Trafodion DBMS via supplied ODBC/JDBC drivers.

• When the client requests to open a connection, Trafodion’s database connection services (DCS) process the request and

assigns the connection to a Trafodion Master SQL process. Trafodion uses Zookeeper to coordinate and manage the

distribution of connection services across the cluster for load-balancing purposes as well as to ensure that a client can

immediately reconnect in the event the assigned Master process should fail.

• The Master process is responsible for coordinating the execution of SQL statements passed from the client application.

• The Master calls upon the Compiler and Optimizer process (CMP) to parse, compile, and generate the optimized execution

plan for the SQL statements.

• If the optimized plan calls for parallel execution, the Master divides the work

among Executive Server Processes (ESP) to perform the work in parallel on

behalf of the Master process. The results are passed back to the Master for

consolidation. In some situations where there a highly complex plan specified

(e.g. large n-way joins or aggregations), multiple layers of ESPs may be

requested. If a non-parallel plan is generated, then the Master calls upon

HBase services directly for optimal performance.

• For distributed transaction protection services the Trafodion DTM service is

called upon to ensure the ACID protection of transactions across the Hadoop

cluster. The DTM calls upon a Trafodion supplied HBase TRX service that

provides transaction resource management on behalf of HBase.

• Last, but not least, vanilla HBase, HBase-trx, and HDFS services are called upon

by either the Master or ESP processes using standard and native API’s to

complete the I/O requests i.e. retrieving and maintaining the database objects.

Where appropriate Trafodion will push down SQL execution into the HBase layer using Filters or Coprocessors.

Trafodion’s optimizer technology

Optimizer technology represents one of Trafodion’s greatest sources of differentiation versus alternative SQL-on-HBase

products. There are two primary areas to call out: the first is the extensible nature of the optimizer to adapt to change and

add improvements and the second is the sophistication and maturity level of the optimizer to choose the best optimized

plan for execution.

Extensible optimizer technology

Trafodion’s optimizer is based on the Cascades optimization framework as authored by HP’s own Goetz Graefe. Cascades is

recognized as one of the most advanced and extensible optimizer frameworks available. The Cascades framework is a

hybrid optimization engine in that it combines logical and physical operator transformation rules with costing models to

generate the Trafodion Optimizer.

New rules or new costing models can be easily added or changed to generate an improved optimizer. In this way, the

optimizer can quickly evolve and new operators can be rapidly added or changed to generate improved SQL optimization

plan generation.

Optimized execution plans based on statistics

The second area of differentiation is the sophistication and maturity level of Trafodion’s optimizer technology. First let’s

explain the role of the various elements of the optimizer:

SQL Normalizer – the parsed SQL statement is passed to the normalizer which performs unconditional transformations,

including subquery transformations, of the SQL into a canonical form which renders the SQL in a form that can be optimized

internally.


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SQL Analyzer - analyzes alternative join connectivity patterns, table access paths

and methods, matching partition information, etc. to be used by the optimizer’s

rules. The results are passed to the plan generator for consideration in costing

various plan alternatives.

Table Statistics – captured equal-height histogram statistics identifies data

distributions for column data and correlations between columns. Sampling is

used for large tables to reduce the overhead of generating the statistics.

Cardinality Estimator - cardinalities, data skew, and histograms are computed for

intermediate results throughout the operator tree.

Cost Estimator - estimates Node, I/O, and message cost for each operator while

accounting for data skew at the operator level.

Plan Generator - using cost estimates the optimizer considers alternative plans and chooses the plan which has the lowest

cost. Where feasible the optimizer will elect plans that incorporate SQL pushdown, sort elimination, and in-memory storage

vs. overflow to disk. Also it determines the optimal degree of parallelism including non-parallel plans.

In summary, the optimizer is designed to choose the execution plan that minimizes the system resource used and delivers

the best response time. It provides optimizations for both operational transactions and reporting workloads.

Trafodion’s data flow SQL executor technology with optimized DOP

Trafodion’s SQL executor uses a dataflow and scheduler-driven task model to

execute the optimized query plan. Each operator of the plan is an

independent task and data flows between operators through in-memory

queues (up and down) or by interprocess communication. Queues between

tasks allow operators to exchange multiple requests or result rows at a time.

A scheduler coordinates the execution of tasks and runs whenever it has data

in one of its input queues. Trafodion’s executor model is starkly different

from alternative SQL-on-Hadoop DBMS that store intermediate results on

disk—for example, spool space. In most cases, the Trafodion executor is able

to process queries with data flowing entirely through memory, providing

superior performance and reduced dependency on disk space and I/O

bandwidth. The executor incorporates several types of parallelism, such as:

• Partitioned parallelism which is the ability to work on multiple data partitions in parallel. In a partitioned parallel plan,

multiple operators all work on the same plan. Results are merged by using multiple queues, or pipelines, enabling the

preservation of the sort order of the input partitions. Partitioning is also called “data parallelism” because the data is the

unit that gets partitioned into independently executable fractions.

• Pipelined parallelism is an inherent feature of the executor resulting from its dataflow architecture. This architecture

interconnects all operators by queues with the output of one operator being piped as input to the next operator, and so

on. The result is that each operator works independently of any other operator, producing its output as soon as its input

is available. Pipelining occurs naturally and is engaged in almost all query plans.

• Operator parallelism is also an inherent feature of the executor architecture. In operator parallelism, two or more

operators can execute simultaneously, that is, in parallel. Except for certain synchronization conditions, the operators

execute independently.

Trafodion naturally provides parallelism without special processing such as Hadoop map-reduce programming or coding on

the part of the application client. An individual query plan produced by the optimizer can contain any combination of

partitioned, pipelined, or operator parallelism. The degree of parallelism at any plan stage may vary depending on the

optimizer’s heuristics.

Trafodion optimizations for transactional SQL workloads

Trafodion provides many compile and run-time optimizations for varying operational workloads ranging from singleton row

accesses for OLTP like transactions to highly complex SQL statements used for operational reporting purposes. Figure 4

depicts a number of these optimization features:

• A Type 2 JDBC driver may be used which provides the client direct JNI access to HBase services to minimize service times


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• For many OLTP like transactions, the Master can issue “directed” key access requests to HBase without needing

intermediate ESP processes.

• For transactions including highly complex SQL statements (e.g. n-way joins or aggregations requiring rebroadcasting or

redistribution of data), a parallel plan involving ESPs or multi-layers of ESP’s can be used to significantly reduce the

service time.

Additional optimizations include:

• Masters and ESPs are retained after a connection is dropped and can be reused thereby eliminating the startup and

shutdown overhead.

• Compiled SQL plans are cached thereby eliminating unnecessary recompilation overhead.

• SQL pushdown using standard HBase services such as filters (e.g. start-stop key predicates) and coprocessors (e.g. count

aggregates).

• Secondary index support.

• A patented access method known as the

Multidimensional Access Method (MDAM) to

accelerate row retrieval performance using

“dimensional” predicates. For example assume

you have a table where the row-key is Week,

Item, and Store but the application supplies only

Item and Store predicates. Without MDAM, this

would mean that the the DBMS must perform a

full table scan or a secondary index on item and

store would have to be created. In contrast, MDAM

utilizes the inherent HBase clustering row-keys to

issue a series of probes and range jumps through

the table reading only the minimal set of rows

required to process the SQL statement. MDAM

usage can be extended to a broad range of data

retrieval requests (e.g. IN lists on multiple key

index columns, NOT equal (<>) predicates,

multivalued predicates, etc.) thus improving

response times and reducing the need for

additional secondary indexes. It is also used to

access tables with a “salted” row key efficiently.

• Rowsets support which is the ability to batch multiple SQL statements in a single request thus reducing the number of

message exchanges between the client and the database engine.

• Availability enhancements including: service persistence (via Zookeeper) and automatic query resubmission.

Figure 5 below summarizes many of the Trafodion optimizations discussed to this point. This is proof that Trafodion

provides optimizations for both operational transaction workloads that typically have very stringent response time

requirements (e.g. sub-second in nature) as well as operational query and reporting workloads that typically have more

relaxed response time requirements (e.g. minutes to hours) and may include SQL statements that require highly complex

SQL operations that are best run in a parallel manner.

Figure 4. Optimized parallel execution


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Figure 5. Trafodion workload optimizations

Trafodion innovation - Distributed Transaction Management

Vanilla HBase provides only single table, row level ACID protection. Trafodion’s distributed transaction management (DTM)

in combination with the HBase-TRX service extends transaction protection to transactions spanning multiple SQL

statements, multiple tables, or multiple rows of a single table. Additionally Trafodion DTM provides protection in a

distributed cluster configuration across multiple HBase regions using an inherent 2-phase commit protocol. Transaction

protection is automatically propagated across Trafodion components and processes. Trafodion eliminates the two-phase

commit protocol overhead for read-only transactions and transactions updating only a single row. In the latter case, native

HBase ACID protection is used.

The DTM provides support for implicit (auto-commit) and explicit (BEGIN, COMMIT, ROLLBACK WORK) transaction control.

Using HBase’s Multi-Version Concurrency Control (MVCC) algorithm, Trafodion allows multiple transactions to be accessing

the same rows concurrently. However, in the case of update, the first transaction to complete wins and other transactions

are notified at commit that the transaction failed due to update conflict.

High availability and data integrity features

Trafodion leverages the inherent availability and data integrity features of HBase and HDFS as shown in the chart below.

Additionally, Trafodion can leverage any Hadoop distribution provided enterprise-class availability extensions that may be

offered.

On top of the HBase and HDFS offered features, Trafodion provides a number of high availability features including:

• Persistent connectivity services that ensure that a client is able to reestablish a connection in the event it’s DCS service

fails

• Automatic query resubmission (AQR) which resubmits a failed SQL statement in the event the statement fails inflight

Summary of Trafodion benefits

Trafodion delivers on the promise of a full featured and optimized transactional SQL-on-HBase DBMS solution with full

transactional data protection. This combination of HBase and an enterprise-class transactional SQL engine overcomes

Hadoop’s weaknesses in terms of supporting operational workloads.


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Customers gain the following recognized benefits:

• Ability to leverage their in-house SQL learnings and expertise versus having to learn complex map/reduce programming.

• Seamless support for existing and new customer written or ISV operational applications drives investment protection and

improved development productivity.

• Workload optimizations provide the foundation for the delivery of next generation real-time transaction processing

applications.

• Guaranteed transactional consistency across multiple SQL statements, tables, and rows.

• Complements exisiting Hadoop investments and benefits - reduced cost, scalability, and elasticity.

• All with open source project sponsorship!

© Copyright 2015 Esgyn Corp. The information contained herein is subject to change without notice. The only warranties for Esgyn products and services are

set forth in the express warranty statements accompanying such products and services. Nothing herein should be construed as constituting an additional

warranty. Esgyn shall not be liable for technical or editorial errors or omissions contained herein.

July 2015

Where to go for more information

Resources, contacts, or additional links

Learn more at https://wiki.trafodion.org/wiki/index.php/

Email questions to [email protected]

https://wiki.trafodion.org/wiki/index.php/

mailto:[email protected]

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Technical white paper Trafodion - Home - Esgyn€¦ · Technical white paper | Trafodion 2 Trafodion Apache Trafodion is an open source initiative to deliver an enterprise class SQL-on-HBase