Data Stax DBAs Guide To No SQL

User Manual: Pdf

Open the PDF directly: View PDF PDF.
Page Count: 41

DBA'S GUIDE TO NOSQL
DataStax Enterprise
THE ENLIGHTENED DBA
Smashwords Edition
Copyright © 2016 The Enlightened DBA
This ebook is licensed for your personal enjoyment only. This ebook may not be re-sold
or given away to other people. If you would like to share this ebook with another person,
please purchase an additional copy for each person you share it with. If you're reading
this book and did not purchase it, or it was not purchased for your use only, then you
should return to Smashwords.com and purchase your own copy. Thank you for respecting
the hard work of this author.
Ebook formatting by www.ebooklaunch.com
Table of Contents
INTRODUCTION
WHY NOSQL?
NOSQL 101
Types of NoSQL Databases
What are the Advantages of NoSQL Over an RDBMS?
Deciding Between an RDBMS and NoSQL
A NoSQL Example - Apache Cassandra
What Makes Cassandra Ideal for Modern Cloud Applications
Top Use Cases
Architecture Overview
Writing and Reading Data
Data Distribution and Replication
Automatic Data Distribution
Replication Basics
Multi-Data Center and Cloud Support
Using Cassandra in Production Environments
NoSQL and Hadoop: A Comparison
DATA AND OBJECT MANAGEMENT
Data Model Overview
Cassandra Objects
Cassandra Query Language
Transaction Management
DBA Query and Management Tools
SECURITY MANAGEMENT
Authentication
Permission Management
Encryption
Data Auditing
MANAGING AVAILABILITY AND MULTIPLE DATA CENTERS
How to Ensure Constant Availability
Multi-Data Center and Cloud Options
ANALYZING AND SEARCHING DATA
Real Time and Batch Analytics
External Hadoop and Spark Support
Searching Data
Workload Management for Analytics and Search
BACKUP AND RECOVERY
Using Replication and Multi-Data Center for Backup and Recovery
Backing up Cassandra
Restoring Data
PERFORMANCE MANAGEMENT
Monitoring Basics
Advanced Command Line Performance Monitoring Tools
Visual Database Monitoring
Finding and Troubleshooting Problem Queries
MIGRATING DATA
DBA STRATEGIES FOR IMPLEMENTING NOSQL
Evaluating NoSQL for Your Enterprise
Technical Considerations
Business Requirements
Practical Guidelines for Selecting NoSQL vs. an RDBMS
Deployment Considerations
CONCLUSION
ABOUT DATASTAX
INTRODUCTION
As a database administrator (DBA), your job is to help develop, manage and guard your
company's single most important asset - its information.
The meteoric rise of modern cloud applications – applications that create and leverage
real-time value and run at epic scale - has brought about a change in data management
with an unprecedented transformation to the decades-old way that databases have been
designed and operated. Requirements from cloud applications have pushed beyond the
boundaries of the relational database management system (RDBMS) and have introduced
a new type of database into the DBA's domain - NoSQL.
As a DBA, you may naturally be skeptical of new database systems, having seen
database engines such as object-oriented and OLAP databases come and go. Why should
NoSQL be any different? Further, perhaps you've heard (and maybe even repeated)
assertions about NoSQL databases like,
NoSQL is not secure...
NoSQL is not reliable...
NoSQL is not scalable...
NoSQL is not really being used by anyone in meaningful ways...
Perhaps you've also asked yourself the following questions about NoSQL:
Are NoSQL databases real and ready for serious applications?
What kinds of administration and management work does NoSQL entail?
How are security, backup and recovery, database monitoring and tuning handled?
How do I create databases, objects, and read/write data without SQL?
This guide was created to help answer all these questions and more. In the following
pages, you'll learn exactly what NoSQL is, why it's needed, how it works, what it should
be used for, and (just as importantly) when it shouldn't be used.
You'll also learn how all the key areas of database administration work - database design,
creation, security, object management, backup/recovery, monitoring and tuning, data
migrations, and more - are carried out in a NoSQL database like Apache Cassandra™.
When you're finished, you'll find that the negative rumors you've heard about NoSQL
aren't actually true, and how being a DBA for NoSQL platforms like Cassandra isn’t
hard. You'll also understand why having NoSQL database skills makes you even more
valuable as a DBA today.
In fact, you may be interested to know that DBA salaries for administrators who possess
NoSQL and other big data skills are significantly higher than the average RDBMS DBA's
salary, with Cassandra currently leading the way for the NoSQL jobs according to a 2016
survey by Dice.
Now, let's get started.
WHY NOSQL?
The RDBMS has been the de-facto standard for managing data since it first appeared
from IBM in the mid-1980s. The RDBMS really exploded in the 1990s with Oracle,
Sybase, Microsoft SQL Server, and other similar databases appearing in the data centers
of nearly every enterprise - databases you likely use today.
With the first wave of Web applications, open source relational database management
systems (RDBMS) such as MySQL and Postgres emerged and became a standard at
many companies that desired alternatives to expensive proprietary databases sold by
vendors such as Oracle.
However, it wasn't long before things began to change, and the application and data
center requirements of key Internet players like Amazon, Facebook, and Google began to
outgrow the RDBMS for certain types of applications. The need for more flexible data
models that supported agile development methodologies and the requirements to
consume large amounts of fast-incoming data from millions of cloud applications users
around the globe - while maintaining extreme amounts of performance and uptime -
necessitated the introduction of a new data management platform.
Enter NoSQL.
Today, with every company utilizing modern cloud applications, the data problems
originally encountered by the Internet giants have become common issues for every
company, including yours. This means that you and your team of database administrators
must realize that it is no longer a question of if you will be deploying and managing
NoSQL database systems, but when, and how much of your company's data will
eventually be stored on NoSQL platforms.
NOSQL 101
This chapter introduces the basics of NoSQL and then dives into a DBA's perspective on
the most scalable and performant NoSQL database in the market today, Apache
Cassandra.
Types of NoSQL Databases
There are different types of NoSQL databases, with the primary difference characterized
by their underlying data model and method for storing data. The main categories of
NoSQL databases are:
•Tabular - Also known as wide-column or wide-row stores, these databases store
data in rows and users are able to perform some query operations via column-
based access. A wide-row store offers very high performance and a highly
scalable architecture. Examples include: Cassandra, HBase, and Google
BigTable.
•Key/Value - These NoSQL databases are some of the least complex as all of the
data within consists of an indexed key and a value. Examples include Amazon
DynamoDB, Riak, and Oracle NoSQL database. Some tabular NoSQL databases,
like Cassandra, can also service key/value needs.
•Document - Expands on the basic idea of key-value stores where "documents"
are more complex, in that they contain data and each document is assigned a
unique key, which is used to retrieve the document. These are designed for
storing, retrieving, and managing document-oriented information, oftentimes
stored as JSON. Examples include MongoDB and CouchDB. Note that some
RDBMS and NoSQL databases outside of pure document stores are able to store
and query JSON documents.
•Graph - Designed for highly complex and connected data, which outpaces the
relationship and JOIN capabilities of an RDBMS. Graph databases are often
exceptionally good at finding commonalities and anomalies among large datasets.
Examples include: DataStax Enterprise Graph and Neo4J.
One trend that is starting to emerge in both the NoSQL and RDBMS markets is the
“multi-model” database. Most database management systems are organized around a
single data model that determines how data can be organized, stored, and manipulated.
By contrast, a multi-model database is designed to support multiple data models against a
single, integrated backend.
The value supplied by multi-model databases is that an enterprise doesn’t have to utilize
multiple data management providers for applications that need to store parts of the
system’s data in different data models, and thus the requirement to shard the application
across different database platforms is removed.
What are the Advantages of NoSQL Over an RDBMS?
While there are hundreds of different "Not Only SQL" (NoSQL) databases offered today,
each with its own particular features and benefits, what you should know from a DBA
perspective is that a NoSQL database generally differs from a traditional RDBMS in the
following ways:
•Data model - while an RDBMS primarily handles structured data in a rigid data
model, a NoSQL database typically provides a more flexible and fluid data model
and can be more adept at serving the agile development methodologies used for
modern cloud applications. Note that one misconception about NoSQL data
models is that they do not handle structured data, which is untrue. Lastly, as
noted above, some NoSQL engines are supporting multiple data models against a
single backend.
•Architecture - whereas an RDBMS is normally architected in a centralized,
scale-up, master-slave fashion, NoSQL systems such as Cassandra operate in a
distributed, scale-out, "masterless" manner (i.e. there is no 'master' node).
However, some NoSQL databases (e.g. MongoDB, HBase) are master-slave in
design.
•Data distribution model - because of their master-slave architectures, an
RDBMS distributes data to slave machines that can act as read-only copies of the
data and/or failover for the primary machine. By contrast, a NoSQL database like
Cassandra distributes data evenly to all nodes making up a database cluster and
enables both reads and writes on all machines. Furthermore, the replication model
of an RDBMS (including master-to-master) is not designed well for wide-scale,
multi-geographical replication and synchronization of data between different
locales and cloud availability zones, whereas Cassandra's replication was built
from the ground up to handle such things.
•Availability model - an RDBMS typically uses a failover design where a master
fails over to a slave machine, whereas a NoSQL system like Cassandra is
masterless and provides redundancy of both data and function on each node so
that it offers continuous availability with no downtime versus simple high
availability in the way an RDBMS does.
•Scaling and Performance model - an RDBMS typically scales vertically by
adding extra CPU, RAM, etc., to a centralized machine, whereas a NoSQL
database like Cassandra scales horizontally by adding extra nodes that deliver
increased scale and performance in a linear manner.
There’s little doubt that relational database management systems (RDBMS) will be
around for a long time and are exactly the right kind of database for handling centralized
applications that require sophisticated transaction handling. But it’s also true that NoSQL
databases are likely to better support widely distributed cloud applications and their
specific use cases.
Deciding Between an RDBMS and NoSQL
How do you decide when to use an RDBMS and when to use a specific type of NoSQL
database? In short, an RDBMS is great for centralized applications that need ACID
transactions and whose data fits well within the relational data model. The following
chart provides a general comparison between the characteristics that point towards an
RDBMS vs. those that signal a NoSQL database may be a better choice:
Figure 1 – RDBMS and NoSQL Comparison
Looking at the data model requirements is another tactic to use when evaluating an
RDBMS vs. NoSQL. Certain NoSQL databases require the denormalization of data and
aren’t concerned with the relationships between data entities whereas others are built to
handle complex and very intense data relationship scenarios:
Figure 2 – The data model continuum by data complexity and connectedness
RDBMS and Graph (NoSQL) databases are at the high end of the data model continuum
where the relationships between data are concerned and are somewhat similar in their
base characteristics:
One of the key differences between a graph database (NoSQL) and an RDBMS is how
relationships between entities/vertexes are prioritized and managed. While an RDBMS
uses mechanisms like foreign keys to connect entities in a secondary fashion, edges in a
graph database are of first order importance. As such, a graph database is more scalable
and performant than an RDBMS when it comes to complex data that is highly connected
(e.g. millions or billions of relationships).
Unlike most other ways of displaying data, graphs are foundationally designed to express
relatedness. Graph databases can uncover patterns that are difficult to detect when using
traditional representations, such as RDBMS tables.
Suggestions for when to use a graph database vs. an RDBMS is the following:
A NoSQL Example - Apache Cassandra
Now that you have a background on how NoSQL differs from an RDBMS, let's look a
little more closely from a DBA's point of view at how a NoSQL database like Cassandra
functions and discuss the above characteristics in detail.
Apache Cassandra is a massively scalable open source NoSQL database. It delivers
continuous availability, linear scale performance, operational simplicity and easy data
distribution across multiple data centers and cloud availability zones. Cassandra was
originally developed at Facebook and sports a design combining capabilities from
Amazon's Dynamo and Google's Bigtable architectures. It was open sourced in 2008.
What Makes Cassandra Ideal for Modern Cloud Applications
Cassandra provides a number of key features and benefits to facilitate the development
and management of cloud applications:
•Massively scalable architecture - Cassandra has a masterless design where all
nodes are the same, providing operational simplicity and easy scale out
capabilities.
•Active everywhere design - all Cassandra nodes may be written to and read from
no matter where they are located.
•Linear scale performance - online node additions produce predictable increases
in performance. For example, if two nodes produce 200K transactions/sec, four
nodes will deliver 400K transactions/sec, and eight nodes, 800K transactions/sec.
•Continuous availability - Cassandra offers redundancy of both data and function,
which supply no single point of failure and constant uptime.
•Transparent fault detection and recovery - nodes that fail can easily be restored
or replaced.
•Flexible and dynamic data models - primarily supports the tabular data model,
but also accommodates JSON well.
•Strong data protection - a commit log design ensures no data loss for incoming
transactions. Also, built-in security with easy backup/restore keeps data protected.
•Basic transaction support with tunable data consistency - Cassandra supports
the atomicity, isolation and durability of transactions (including batch) with
strong or eventual data consistency supplied across a widely distributed cluster.
•Multi-data center replication - Cassandra provides strong cross data center (in
multiple geographies) and multi-cloud availability zone support for writes/reads.
•Data compression - data compressed up to 80% without performance overhead
helps save on storage costs.
•CQL (Cassandra Query Language) - a SQL-like language that makes moving
from an RDBMS easy.
Top Use Cases
While Cassandra is a general purpose NoSQL database used for a variety of different
applications in all industries, there are a number of use cases where the database excels
over most any other option. These include:
•Cloud Applications – Applications that require the wide distribution of data, no
downtime, predictable performance no matter the location of the user, and easy
scale make good targets for Cassandra.
•Internet of Things (IOT) - Cassandra is good for consuming and analyzing lots
of fast-incoming data from devices, sensors and similar mechanisms that exist in
many different locations.
•Product catalogs and retail apps - For retailers that need durable shopping cart
protection, fast product catalog input and lookups, and similar retail application
support, Cassandra is a good choice.
•User activity tracking and monitoring - Media, gaming and entertainment
companies use Cassandra to track and monitor the activity of users' interactions
with their movies, music, games, website and online applications.
•Messaging - Cassandra serves as the database backbone for numerous mobile
phone, telecommunication, cable/wireless, and messaging providers' applications.
•Social media analytics and recommendation engines - Online companies,
websites, and social media providers use Cassandra to ingest, analyze, and
provide analysis and recommendations to their customers.
•Other time series based applications - because of Cassandra's fast write
capabilities and wide-row design, it is well suited for most any time series based
application.
Architecture Overview
The architecture of Cassandra allows the database to scale and perform with no
downtime. Rather than using a legacy RDBMS master-slave or a manual and difficult-to-
maintain sharded design, Cassandra has a masterless "ring" distributed architecture that is
elegant, and easy to set up and maintain.
Figure 3 - Cassandra sports a masterless "ring" architecture.
In Cassandra, all nodes are the same; there is no concept of a master node, with all nodes
communicating with each other via a gossip protocol.
Cassandra's built-for-scale architecture allows it to handle large amounts of data and
thousands of concurrent users/operations per second, across multiple data centers, as
easily as it can manage much smaller amounts of data and user traffic. To add more
capacity, you simply add new nodes in an online fashion to an existing cluster.
Cassandra's architecture also means that, unlike other master-slave or sharded systems, it
has no single point of failure and therefore offers true continuous availability and uptime.
Writing and Reading Data
One of Cassandra's hallmarks is its fast I/O operation capability for both writing and
reading data.
Data is written to Cassandra in a way that provides both full data durability and high
performance. From a high level perspective, data written to a Cassandra node is first
recorded in a commit log and then written to a memory-based structure called a
memtable. When a memtable's size exceeds a configurable threshold, the data is flushed
to disk and written to an SStable (sorted strings table), which is immutable.
Figure 4 - The Cassandra write path.
Because of the way Cassandra writes data, many SStables can exist for a single
Cassandra table/column family. A process called compaction for a node occurs on a
periodic basis that coalesces multiple SStables into one for faster read access.
Reading data from Cassandra involves a number of processes that can include various
memory caches and other mechanisms designed to produce fast read response times.
For a read request, Cassandra consults a bloom filter that checks the probability of a table
having the needed data. If the probability is good, Cassandra checks a memory cache that
contains row keys and either finds the needed key in the cache and fetches the
compressed data on disk, or locates the needed key and data on disk and then returns the
required result set.
Figure 5 - The Cassandra read path.
Data Distribution and Replication
While the prior section provides a general overview of read and write operations in
Cassandra, the actual I/O activity that occurs is somewhat more sophisticated, due to the
database's masterless architecture. Two concepts that impact read and write activity are
the chosen data distribution and replication models.
Automatic Data Distribution
While an RDBMS and some NoSQL databases necessitate manual and developer-driven
methods for distributing data across multiple machines that make up a database (i.e.
sharding), Cassandra automatically distributes and maintains data across a cluster so you
as a DBA don't have to.
Cassandra uses a partitioner to determine how data is distributed across the nodes that
make up a database cluster. A partitioner is a hashing mechanism that takes a table row's
primary key, computes a numerical token for it, and then assigns it to one of the nodes in
a cluster.
While Cassandra has multiple partitioners that can be chosen, the default partitioner is
one that randomizes data across a cluster and ensures an even distribution of all data.
Cassandra also automatically maintains the balance of data across a cluster even when
existing nodes are removed or new nodes are added to a system.
Replication Basics
Unlike many other database management systems, replication in Cassandra is very
straightforward and simple to configure and maintain. Most Cassandra users agree that
the replication model is one of the features that help the database stand out from other
RDBMS or NoSQL options.
A running Cassandra database cluster can have one or more keyspaces, which are
analogous to a Microsoft SQL Server or MySQL database. It is at the keyspace level that
replication is configured, allowing different keyspaces to have different replication
models.
Cassandra is able to replicate data to multiple nodes in a cluster, which helps ensure
reliability, continuous availability and fast I/O operations. The total number of data
copies that are replicated is referred to as the replication factor. For example, a
replication factor (RF) of 1 means that there is only one copy of each row in a cluster,
whereas a replication factor of 3 means three copies of the data are stored across the
cluster.
Once a keyspace and its replication have been created, Cassandra automatically
maintains that replication even when nodes are removed, added or go down and become
unavailable for receiving data requests. This equates to there being no replication
babysitting you need to do as a DBA.
Cassandra's replication is both simple to configure and powerful in that it supports a wide
range of replication capabilities such as replicating data to different hardware racks
(reducing database downtime due to hardware failures) and multiple data centers in
different geographic locations as well as the cloud.
Multi-Data Center and Cloud Support
A very popular aspect of Cassandra's replication is its support for multiple data centers
and cloud availability zones. Many users deploy Cassandra in a multi-data center and
cloud availability zone manner to ensure constant uptime for their applications and to
supply fast read/write data access in localized regions.
You can easily set up replication so that data is replicated across many data centers with
users being able to read and write to any data center they choose and the data being
automatically synchronized across all centers.
You can also choose how many copies of your data exist in each data center (e.g. 2
copies in data center 1; 3 copies in data center 2) Hybrid deployments of part on-premise
data centers and part cloud are also supported.
Using Cassandra in Production Environments
As a DBA, you have a responsibility to ensure that the database software you use will
work and perform as expected in production environments. To provide that type of
guarantee, most NoSQL databases have a commercial software vendor that offers a
production-certified version of the database, which often times possess various enterprise
features that the open source version does not.
For Cassandra, DataStax provides DataStax Enterprise as the commercial software
offering. As a DBA, you should be aware that DataStax Enterprise (DSE) provides the
following benefits over the open source version of Cassandra that help you manage,
secure, and optimize your database systems:
•A production-certified version of Cassandra that is heavily tested and ready for
enterprise environments.
•Multi-model database capabilities with support for the key value, tabular, JSON /
Document, and graph data models, all of which inherit the capabilities of
Cassandra and additional commercial functionality that follows.
•Advanced security with external security software support, encryption and data
auditing.
•Integrated analytics, including integration with external Hadoop and Spark
platforms.
•Integrated enterprise search on stored data.
•Workload isolation and data replication that ensures OLTP, analytics, and search
workloads do not compete with each other for data or compute resources.
•In-memory database option for both OLTP and analytic workloads.
•Advanced replication that handles data distribution among different clusters in a
hub-and-spoke fashion.
•Tiered storage that provides automatic movement of data between different
storage media (e.g. SSD’s, spinning disks).
•Multi-instance functionality that assists with running multiple instances of the
software on single, large servers.
•Automatic management services that transparently automate numerous database
maintenance and performance monitoring/management tasks.
•Visual management and monitoring tools that work from any device (laptop,
tablet, smart phone).
•Around-the-clock expert support.
•Certified software updates.
NoSQL and Hadoop: A Comparison
You've no doubt heard about Hadoop and perhaps your company is already using it to
handle various new data warehousing projects. Perhaps you're wondering how Hadoop
differs from NoSQL.
Apache HadoopTM is an open source software project that enables the distributed
processing of large data sets, and uses a scale-out architecture that stores and processes
data across many machines. Hadoop is an ecosystem umbrella term that encompasses
many different software components.
In general, Hadoop is not a database, but is instead a framework primarily devoted to
handling modern data warehousing and analytics "data lake" use cases. Hadoop does
offer a NoSQL database as part of its framework (HBase), but it is used mostly for data
warehousing situations.
By contrast, a NoSQL database like Cassandra is an operational / transactional database
used for cloud applications.
DATA AND OBJECT MANAGEMENT
This section takes a look at the core Cassandra's data model, what data objects are used
for managing data, CQL (Cassandra Query Language), and how transactions are handled
in the database.
Data Model Overview
Achieving success with Cassandra almost always comes down to getting two things
right:
1. The data model
2. The selected hardware, especially the storage subsystem
Cassandra is a wide row / tabular database that uses a highly denormalized model
designed to quickly capture and query data. There are no concepts of foreign keys,
referential integrity, or joins in Cassandra (note that using Spark with Cassandra provides
join capability through SparkSQL).
Although Cassandra has objects that resemble an RDBMS (e.g. tables, primary keys,
indexes), data should not be modeled in a legacy entity-relationship-attribute fashion as is
done with a relational database. Modeling data in Cassandra is done by understanding
what questions you will need to ask the database up front, whereas in an RDBMS, you
are likely not used to addressing such things until after all entities, relationships, and
attributes are documented.
Unlike an RDBMS that penalizes the use of many columns in a table, Cassandra is highly
performant with tables that have hundreds of columns. As a DBA, you may be used to
highly normalized, third normal form models that you translate into a set of physical
tables and their accompanying indexes and such. With Cassandra, you will oftentimes
instead have wide row tables with some data duplication between tables.
Creating your physical objects, however, still looks very much like what you carry out in
an RDBMS. For example, a new table defining users for an application might look like
the following:
Cassandra Objects
The basic objects you will use in Cassandra include:
•Keyspace - a container for data tables and indexes; analogous to a database in
many relational database management systems (RDBMS). It is also the level at
which replication is defined.
•Table - somewhat like an RDBMS table only much more flexible and capable of
handling all modern data types.
•Primary key - used to uniquely identify a row in a table and also distribute a
table's rows across multiple nodes in a cluster.
•Index - similar to an RDBMS index in that it speeds read operations.
•User - a login account used to access data objects.
Cassandra Query Language
Earlier versions of Cassandra solely used an interface called Thrift to create database
objects and manipulate data. Today, the Cassandra Query Language (CQL) has become
the primary interface used for interacting with a Cassandra database cluster.
CQL very closely resembles SQL (Structured Query Language) used by all relational
database management systems (RDBMS). Because of this similarity, your learning curve
will be greatly reduced.
DDL (e.g. CREATE, ALTER, DROP), DML (INSERT, UPDATE, DELETE,
TRUNCATE), and query (SELECT) operations are all supported in the manner to which
you are accustomed.
CQL datatypes also reflect RDBMS syntax with numerical (int, bigint, decimal, etc.),
character (ascii, varchar, etc.), date (timestamp, etc.), unstructured (blob, etc.), and
specialized datatypes (JSON, etc.) being supported.
Learn more about CQL on the documentation page at www.DataStax.com.
Transaction Management
While Cassandra does not offer complex/nested transactions in the same way that your
legacy RDBMS offer ACID transactions, it does offer the "AID" portion of ACID, in that
data written is atomic, isolated, and durable. The "C" of ACID does not apply to
Cassandra, as there is no concept of referential integrity or foreign keys.
With respect to data consistency, Cassandra offers tunable data consistency across a
database cluster. This means you can decide exactly how strong (e.g., all nodes must
respond) or eventual (e.g., just one node responds, with others being updated eventually)
you want data consistency to be for a particular transaction, including transactions that
are batched together. This tunable data consistency is supported across single or multiple
data centers, and you have a number of different consistency options from which to
choose.
Moreover, consistency can be handled on a per operation basis, meaning you can decide
how strong or eventually consistent it should be per the SELECT, INSERT, UPDATE,
and DELETE operation. For example, if you need a particular transaction available on all
nodes throughout the world, you can specify that all nodes must respond before a
transaction is marked complete. On the other hand, a less critical piece of data (e.g., a
social media update) may only need to be propagated eventually, so in that case, the
consistency requirement can be greatly relaxed.
Cassandra also supplies "lightweight transactions" (or compare and set). Using and
extending the Paxos consensus protocol (which allows a distributed system to agree on
proposed data modifications without the need for any one 'master' database or two-phase
commit), Cassandra offers a way to ensure a transaction isolation level similar to the
serializable level offered by an RDBMS for situations that need it.
DBA Query and Management Tools
As a DBA coming from the RDBMS world, you likely use many command line and
visual tools for interacting with the databases you manage. The same kind of tools are
available to you with Cassandra.
Various command line utilities are provided for handling administration functions (e.g.
the nodetool utility), loading data, and using CQL to create and query database objects
(the CQL shell, which is much like Oracle's SQL*Plus or the MySQL shell).
In addition, graphical tools are provided for running CQL commands against database
clusters (e.g. DataStax DevCenter, DataStax Studio) and visually
creating/managing/monitoring your clusters (DataStax OpsCenter).
Figure 6 - DataStax OpsCenter, used for visual database administration.
Figure 7 - DataStax DevCenter, used for visually querying databases.
SECURITY MANAGEMENT
As a DBA, data security is one of your top priorities. One of the myths of NoSQL
databases like Cassandra is that they don't offer the security needed in enterprise
production environments. In this section, we'll review Cassandra's security capabilities.
Authentication
Cassandra supports internal-based authentication that allows you to easily create users
who can be authenticated to Cassandra database clusters. You'll find the authentication
framework extremely familiar - it uses the RDBMS-style CREATE/ALTER/DROP
USER commands to create/manage with passwords that will then be internally handled
by Cassandra. A default superuser, 'cassandra', is supplied by default to initially enable
the security authentication definition process.
You can also use external, 3rd party security packages like Kerberos, LDAP, and Active
Directory to manage security in DataStax Enterprise.
Permission Management
Object permission/authorization capabilities for Cassandra utilize the very familiar
GRANT/REVOKE security paradigm - something you should have no problem using as
a DBA. Control over DDL, DML, and SELECT operations are all handled via the
granting and revoking of user privileges.
Note that a GRANT may be done with or without the GRANT OPTION, which allows
the user receiving the grant to provide the same privileges on that object to other users
just as how it occurs in the RDBMS world.
Encryption
There are multiple levels of encryption offered in both Cassandra and DataStax
Enterprise that you can use to protect data. First, Cassandra includes an optional
encrypted form of communication from a client machine to a database cluster. Client to
server SSL ensures data in flight is not compromised and is securely transferred
back/forth from client machines.
Next, node-to-node encryption can be used as well to ensure data is protected as it is
transferred between nodes in a database cluster.
Lastly, transparent data encryption (TDE) in DataStax Enterprise protects data at rest
from being stolen and used in an unauthorized manner. You can encrypt tables with AES
128 being the default, although other encryption algorithms can be used.
Encryption is transparent to all end user activities; data may be read, inserted, updated,
etc., with nothing having to change on the application end.
Data Auditing
If needed, you can configure data auditing so you can understand what user activities
took place on a particular node or entire cluster. Data auditing allows for a "who looked
at what/when, who changed what/when" type of documentation that many large-scale
enterprises need to have in order to comply with various internal or external security
policies.
The granularity of activities that can be audited include:
• All activity (DDL, DML, queries, errors)
• DML only
• DDL only
• Security changes (e.g. assigning/revoking privileges, dropping users)
• Queries only
• Errors only (e.g. login failures)
You can also omit certain keyspaces from being audited if you choose and only focus on
keyspaces in production or those that are of particular interest. Audit data can be written
to log files or Cassandra tables and queried via CQL.
MANAGING AVAILABILITY AND MULTIPLE DATA CENTERS
Another key aspect of your job as a DBA is to ensure the databases you manage are
always available for the applications that use them. One thing you will like about
Cassandra is that ensuring constant uptime is easy. There is no need for specialized, add-
on log shipping software such as Oracle Dataguard.
Further, distributing data to multiple geographies and across various cloud providers is
simple and straightforward with Cassandra.
How to Ensure Constant Uptime
As previously discussed, Cassandra sports a masterless architecture where all nodes are
the same; and it has been built from the ground up with the understanding that outages
and hardware failures will occur. To overcome those and similar issues, Cassandra
delivers redundancy in both data and function to a database cluster with all nodes being
the same.
Where data operations are concerned, any node in a cluster may be the target for both
reads and writes. Should a particular node go down, there is no hiccup in the cluster at all,
as any other node may be written to, with reads served from other nodes holding copies
of the downed node's data.
To ensure constant access to data, you should configure Cassandra's replication to keep
multiple copies of data on the nodes that comprise a database cluster. The number of data
copies is completely up to you, with three being the most commonly used in production
Cassandra environments.
Should a node go down, new or updated information is simply written to another node
that keeps a copy of that data. When the downed node is brought back online, it
automatically syncs with other nodes holding its data so that it is brought back up to date
in a transparent fashion.
Multi-Data Center and Cloud Options
Cassandra is the leading distributed database for multi-data center and cloud support.
Many production Cassandra systems consist of a database cluster that spans multiple
physical data centers, cloud availability zones, or a combination of both. Should a large
outage occur in a particular geographical region, the database cluster continues to operate
as normal with the other data centers assuming the operations previously directed at the
now downed data center or cloud zone. Once the downed data center comes back online,
it syncs with the other data centers and makes itself current.
Figure 8 - A single Cassandra database cluster can span multiple data centers and the
cloud.
An additional benefit of having a single cluster that spans multiple data centers and
geographies is that data can be read and written to incredibly quickly in each location,
thus keeping performance very high for the customers it serves in those locations.
ANALYZING AND SEARCHING DATA
Many applications have requirements that their underlying transactional database easily
service analytic and search operations. As a DBA, you are likely familiar with analytic
capabilities that can be run via SQL and full-text search options in an RDBMS, and might
wonder how the same things are handled in Cassandra.
Real Time and Batch Analytics
Because Cassandra has a distributed, shared-nothing architecture, the framework for
running analytics on it compared to a centralized RDBMS will be different.
There are two options in DataStax Enterprise that allow you to run analytic operations
easily on Cassandra data. You can run both real-time and batch (i.e. longer running)
analytics on data via the platform's built-in components that utilize Apache Spark for
analytics work.
The analytics capability in the platform provides you with a number of the SQL functions
and abilities that you are used to in the RDBMS world (e.g. joins, aggregate functions).
In addition, analytics can be run across multiple data centers and cloud availability zones.
Built-in continuous availability options are also included.
External Hadoop and Spark Support
You also have the ability to connect the data in DataStax Enterprise to an external
Hadoop and/or Spark cluster and run analytic queries on data that combines both the
operational data in Cassandra with historical data stored in a Hadoop deployment such as
Cloudera or Hortonworks (e.g. a single query can join a Cassandra table with a Hadoop
object). If you have used RDBMS connection options such as Oracle's database links or
Microsoft SQL Server's linked servers to integrate external database systems, the concept
is somewhat similar.
Searching Data
Some architects still shard their systems and use something like Cassandra for
operational data management and a separate system and set of software for search
operations. As a DBA, you’d likely prefer to have everything under one roof.
DataStax Enterprise supplies DSE Search, which uses Apache SolrTM as its foundation to
manage search tasks. With DSE Search, you don’t have to shard your application and you
have the typical search bases covered including full-text search, hit highlighting, faceted
search, rich document (e.g., PDF, Microsoft Word) handling and geospatial search.
Search operations can scale out across multiple nodes so you can add more nodes
dedicated to search tasks when the need arises. Multi-data center and cloud support is
built in, as is redundancy for continuous availability.
Workload Management for Analytics and Search
When enabling analytics and search on a database cluster, you have a number of
configuration options available. If you choose, you can run transactional (OLTP),
analytics and search operations on all nodes in a database cluster.
Another deployment methodology includes separating OLTP, analytics, and search
workloads so that each runs on its own series of nodes. This strategy ensures that
differing workloads do not compete with each other for either compute or data resources.
Replication can be set up between all nodes so that data is transparently replicated to each
set of nodes without manual intervention.
This translates into you not having to worry about complex ETL jobs that transfer data
between different systems, as you might be used to doing with an RDBMS.
This also holds true if you are running graph database operations in a cluster –
operational, analytical and search tasks can either be combined or separated across
different nodes.
BACKUP AND RECOVERY
One of your key responsibilities as a DBA is to ensure that proper backup and recovery
procedures are in place should a database become corrupted or a large data loss occurs.
This section describes how backup and recovery processes work on a NoSQL database
like Cassandra.
Using Replication and Multi-Data Center for Backup and Recovery
Some administrators simply use Cassandra's built-in replication and multi-data center
capabilities for backup. Because the functionality is native to Cassandra, there is no need
for add-on software (e.g. Oracle Dataguard). Since replication is so easy to use, some
DBA's just create one or more physical or virtual data centers for a cluster and utilize
them for disaster recovery purposes.
While such a strategy can be satisfactory for some situations, it is important to note that it
will not protect you in cases where large amounts of data are deleted, tables are dropped,
and other similar unintended actions are carried out - such activities will be replicated and
applied to the other data centers.
Backing up Cassandra
Cassandra allows you easily backup all keyspaces in a cluster, certain selected keyspaces,
or only desired tables in a keyspace. A backup is called a snapshot in Cassandra.
You can takes snapshots of your cluster via either a command line utility or visually
through DataStax OpsCenter. While you can script your own backups via command line
utilities, OpsCenter provides an easy way to design and schedule your backups.
Figure 9 - DataStax OpsCenter's backup interface.
Note that you can also customize backups in OpsCenter by writing and including scripts
that run both before and after a backup.
Lastly, incremental (only new or changed data versus full) backups are also supported for
Cassandra, although it’s not exactly the same as with an RDBMS. An incremental backup
will backup only changed SStables vs. change rows as in an RDBMS.
Restoring Data
Database recovery operations can be carried out with either command line utilities or
visually through DataStax OpsCenter. Restores can be full, utilize incremental backups,
and also be object-level if needed (e.g. you can only restore one backed up table versus
all tables).
Figure 10 - Restoring a keyspace with OpsCenter.
OpsCenter simplifies restore operations and handles restore tasks on all affected nodes.
PERFORMANCE MANAGEMENT
Monitoring, troubleshooting, and tuning databases are a top priority for you as a DBA.
This section details how you can carry out your performance management tasks on a
NoSQL database like Cassandra.
Monitoring Basics
There are a number of command line utilities that enable you to get a status of your
database clusters as well as general metrics for the network, objects and I/O operations
both at a high level and low level (e.g. table) fashion. For example, the Cassandra
nodetool utility lets you quickly determine the up/down status and current data
distribution of a cluster:
Figure 11 - Checking a cluster's status with the nodetool utility.
Advanced Command Line Performance Monitoring Tools
From a performance metrics standpoint, Cassandra delivers many different statistics that
can be accessed in various ways. If you are coming from an RDBMS like Oracle or
Microsoft SQL Server and are used to performance data dictionaries like Oracle's V$
views or SQL Server' dynamic management tables, the most familiar interface for you is
the one supplied by DataStax Enterprise's Performance Service.
The Performance Service collects, organizes, and maintains an in-depth diagnostic data
dictionary for each cluster. It consists of various tables that can be accessed via any CQL
utility (e.g. the CQL shell utility, DataStax DevCenter) and gives you both high-level and
detailed performance views of how well a cluster is running.
The Performance Service maintains the following levels of performance information:
•System level - supplies general memory, network and thread pool statistics.
•Cluster level - provides metrics at the cluster, data center and node level.
•Database level - provides drill down metrics at the keyspace, table and table-per-
node level.
•Table histogram level - delivers histogram metrics for tables being accessed.
•Object I/O level - supplies metrics concerning 'hot objects' and data on what
objects are being accessed the most.
•User level - provides metrics concerning user activity, top users (those consuming
the most resources on the cluster) and more.
•Statement level - captures queries that exceed a certain response time threshold
along with all their relevant metrics.
You can configure the service to collect nothing, all, or selected performance metrics for
the above categories. Once the service has been configured and is running, statistics are
populated in their associated tables and stored in a special keyspace (dse_perf). You can
then query the various performance tables to get statistics such as the I/O metrics for
certain objects:
(2rows)
Visual Database Monitoring
In addition to monitoring your database clusters from the command line, you can also
easily check the health of all clusters you're managing visually by using DataStax
OpsCenter. OpsCenter gives you both global, at-a-glance dashboards that help you
understand how all clusters under your control are doing, as well as drill down
capabilities into each cluster and its individual nodes.
A global dashboard helps you understand how well all clusters are running and if there
are any alerts or issues for one or more clusters that need your attention:
Figure 12 - Checking OpsCenter's global cluster dashboard.
From the global dashboard, you can drill down into each individual cluster and create
customized monitoring dashboards for the performance metrics you care about the most:
Figure 13 - Examining performance metrics for a single database cluster.
You can also create proactive alerts that notify you far in advance of a problem actually
occurring in one of your clusters:
Figure 14 - Creating an alert in OpsCenter.
In addition, you can utilize built-in expert services like the Best Practice service that will
scan your clusters and provide expert advice on how to configure and tune things for
better uptime and performance:
Figure 15 - OpsCenter's Best Practice service.
These and other capabilities in OpsCenter help monitor and tune database clusters via
any Web browser (laptop, tablet, smart phone) no matter if they are in your own data
center or are running on one of the cloud providers.
Finding and Troubleshooting Problem Queries
As a DBA, you're sometimes called upon to locate a database's worst running queries that
slow the performance of the system as a whole. You'll find this isn't hard to do with
Cassandra.
First, you can use the DataStax Enterprise Performance Service to automatically capture
long-running queries (based on response time thresholds you specify) and then query a
performance table that holds those statements:
In addition, there is a background query tracing utility available that you can use on an
ad-hoc basis. You can choose to trace all statements coming into a database cluster or
only a percentage of them, and then look at the results. The trace information is stored in
the systems_traces keyspace that holds two tables: sessions and events, which can be
easily queried to answer questions such as what the most time-consuming query has been
since a trace was started, and much more.
You can also use the tracing utility much in the same way you do an EXPLAIN PLAN
on an RDBMS query. For example, to understand how a Cassandra cluster will satisfy a
single CQL INSERT statement, you would enable the trace utility from the CQL
command shell, issue your query, and review the diagnostic information provided:
With Cassandra's tracing capabilities, OpsCenter's visual monitoring, DataStax
Enterprise's Performance service and general command line monitoring tools, you will
have most, if not all, of the typical performance tools at your disposal with Cassandra as
you do today with your favorite RDBMS.
MIGRATING DATA
Moving data from an RDBMS or other database to Cassandra is generally quite easy. The
following options exist for migrating data to Cassandra:
•COPY command - CQL provides a copy command (very similar to Postgres)
that is able to load data from an operating system file into a Cassandra table.
•Bulk loader - this utility is designed for more quickly loading a Cassandra table
with a file that is delimited in some way (e.g. comma, tab, etc.) Note: there is a
separate bulk loader available for DSE Graph.
•ETL tools - there are a variety of ETL tools (e.g. Informatica) that support
Cassandra as both a source and target data platform. Many of these tools not only
extract and load data but also provide transformation routines that can manipulate
the incoming data in many ways. A number of these tools are also free to use (e.g.
Pentaho, Jaspersoft, Talend).
DBA STRATEGIES FOR IMPLEMENTING NOSQL
This section provides basic checklists to use when evaluating a NoSQL database for
production environments, guidelines for deciding when NoSQL should be deployed
versus an RDBMS and what deployment scenarios are most common.
Evaluating NoSQL for Your Enterprise
Although not exhaustive, below are technical and business considerations designed to ask
the right questions when evaluating whether a particular NoSQL database is suited for
your production environment:
Technical Considerations
•Can the NoSQL database serve as the primary data source for the intended online
application?
•How safe is the NoSQL database where the possibility of losing critical data is
concerned? Are writes durable in nature by default so that data is safe?
•Is the NoSQL database fault tolerant (i.e., has no single point of failure) and is it
capable of providing not just high availability, but continuous availability?
•Can the NoSQL database easily replicate data between the same and multiple data
centers, as well as different cloud availability zones?
•Does the NoSQL database offer read/write anywhere capabilities (i.e. can any
node in the cluster be written to and read from)?
•Does the NoSQL database provide a robust security feature set?
•Does it support easy-to-create and manage backup and recover procedures?
•Does the NoSQL database require or remove the need for special caching layers?
•Is the NoSQL database capable of managing "big data" and delivering high
performance results regardless of data size?
•Does the NoSQL database offer linear scalability where adding new nodes is
concerned?
•Can new nodes be added and removed online (i.e. without business impact)?
•Does the NoSQL database support key platforms/developer languages?
•Does the NoSQL database provide an SQL-like query language?
•Can the NoSQL database run on commodity hardware with no special hardware
requirements?
•Is the NoSQL database easy to implement and maintain for large deployments?
•Does the NoSQL database provide data compression that supplies real storage
savings?
•Can analytic operations be run easily on the NoSQL database?
•Can the NoSQL database easily interface with and support modern data
warehouses or lakes that utilize Hadoop?
•Can search operations and functions be easily and directly carried out on the
NoSQL database?
•Can the NoSQL database provide workload isolation between online, analytic,
and search operations in a single application?
•Does the database have solid command-line and visual tools for development,
administration, and performance management?
Business Requirements
•Is the NoSQL solution backed by a commercial entity?
•Does the commercial entity provide enterprise 24x7 support and services?
•Does the NoSQL solution have professional online documentation?
•Does the NoSQL solution have referenceable customers across a wide range of
industries that use the product in critical production environments?
•Does the NoSQL database have an attractive cost/pricing structure?
•If open source, does the NoSQL database have a thriving open source
community?
Practical Guidelines for Selecting NoSQL vs. an RDBMS
How do you determine whether a NoSQL database like Cassandra or DSE Graph should
be used for all or part of an application versus an RDBMS? Some basic questions to ask
include:
•Do you need a more flexible data model to manage data that goes beyond the
RDBMS table/row data structure and instead includes a combination of
structured, semi-structured and unstructured data?
•Do you find that complex JOIN operations are overwhelming your RDBMS and
response times are slow because of them?
•Do you care more about the value derived from the relationships that form
between the tables vs. the tables themselves?
•Do you need continuous availability with redundancy in both data and function
across one or more locations versus simple failover for the database?
•Do you need a database that runs over multiple data centers / cloud availability
zones?
•Do you need to handle high velocity data coming in via sensors, mobile devices,
and the like, and have extreme write speed and low latency query speed?
•Do you need to go beyond single machine limits for scale-up and instead go to a
scale-out architecture to support the easy addition of more processing power and
storage capacity?
•Do you need to run different workloads (e.g. online, analytics, search) on the
same data without needing to manually ETL the data to separate
systems/machines?
•Do you need to manage a widely distributed system with minimal staff?
Deployment Considerations
From a practical perspective, as a DBA, how do you go about actually moving to NoSQL
and implementing your first application? In general, there are three ways to deploy a
NoSQL database like Cassandra:
1. New applications: many begin with NoSQL by choosing a new application and
starting from the ground up. Such an approach mitigates the issues of application
rewrites, data migrations, etc.
2. Augmentation: some choose to augment an existing system by adding a NoSQL
component to it. This oftentimes happens with applications that have outgrown an
RDBMS due to scale problems, the need for better availability, or other issues.
Parts of the existing system continue to use the existing RDBMS whereas other
components of the application are modified to utilize the NoSQL database.
3. Full Rip-Replace: for systems that simply are proving too costly from an
RDBMS perspective to keep, or are breaking in major ways due to increases of
user concurrency, data velocity, or data volume from cloud applications, a full
replacement is done with a NoSQL database.
CONCLUSION
This guide has been designed to provide you with a preliminary understanding from a
DBA perspective on the basics of NoSQL, and how a NoSQL database like Apache
Cassandra differs from an RDBMS like Oracle, SQL Server, and MySQL. It has been
written to supply you with an overview of how you will go about designing, managing,
deploying, and monitoring Cassandra or multi-model database systems.
To find out more about Apache Cassandra and DataStax, and to obtain downloads of
Apache Cassandra and DataStax Enterprise software, visit www.datastax.com or send an
email to info@datastax.com.
DataStax Enterprise Edition is completely free to use in non-production environments,
while production deployments require a software subscription be purchased.
ABOUT DATASTAX
DataStax, the leading provider of database software for cloud applications, accelerates
the ability of enterprises, government agencies, and systems integrators to power the
exploding number of cloud applications that require data distribution across datacenters
and clouds, by using our secure, operationally simple platform built on Apache
Cassandra™.
With more than 500 customers in over 50 countries, DataStax is the database technology
of choice for the world’s most innovative companies, such as Netflix, Safeway, ING,
Adobe, Intuit, Target and eBay. Based in Santa Clara, Calif., DataStax is backed by
industry-leading investors including Comcast Ventures, Crosslink Capital, Lightspeed
Venture Partners, Kleiner Perkins Caufield & Byers, Meritech Capital, Premji Invest and
Scale Venture Partners. For more information, visit DataStax.com or follow us
@DataStax. 06.18.16

Navigation menu