This document provides an introduction to Cassandra, including what it is, how it works, and how to model data in Cassandra. Specifically:
- Cassandra is a distributed, decentralized, column-oriented NoSQL database modeled after Amazon Dynamo and Google Bigtable. It is fault-tolerant, scalable, and provides high availability.
- Cassandra uses an eventual consistency model and is optimized for availability over strong consistency. It addresses problems with horizontal scaling in relational databases.
- Data is modeled using keyspaces, column families, rows, columns, and super columns. Common design patterns include materialized views and storing column names as values.
Cassandra is an open source distributed database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Cassandra offers robust support for clusters spanning multiple data-centres,with asynchronous master-less replication allowing low latency operations for all clients.
HBaseCon 2012 | HBase Schema Design - Ian Varley, SalesforceCloudera, Inc.
Most developers are familiar with the topic of “database design”. In the relational world, normalization is the name of the game. How do things change when you’re working with a scalable, distributed, non-SQL database like HBase? This talk will cover the basics of HBase schema design at a high level and give several common patterns and examples of real-world schemas to solve interesting problems. The storage and data access architecture of HBase (row keys, column families, etc.) will be explained, along with the pros and cons of different schema decisions.
HBase In Action - Chapter 04: HBase table designphanleson
HBase In Action - Chapter 04: HBase table design
Learning HBase, Real-time Access to Your Big Data, Data Manipulation at Scale, Big Data, Text Mining, HBase, Deploying HBase
Slides from a brief talk I gave at the local JUG, javaBin. It's about our experiences using Cassandra in a production environment, with some philosophizing here and there.
Most developers are familiar with the topic of “database design”. In the relational world, normalization is the name of the game. How do things change when you’re working with a scalable, distributed, non-SQL database like HBase? This talk will cover the basics of HBase schema design at a high level and give several common patterns and examples of real-world schemas to solve interesting problems. The storage and data access architecture of HBase (row keys, column families, etc.) will be explained, along with the pros and cons of different schema decisions.
Cassandra is an open source distributed database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Cassandra offers robust support for clusters spanning multiple data-centres,with asynchronous master-less replication allowing low latency operations for all clients.
HBaseCon 2012 | HBase Schema Design - Ian Varley, SalesforceCloudera, Inc.
Most developers are familiar with the topic of “database design”. In the relational world, normalization is the name of the game. How do things change when you’re working with a scalable, distributed, non-SQL database like HBase? This talk will cover the basics of HBase schema design at a high level and give several common patterns and examples of real-world schemas to solve interesting problems. The storage and data access architecture of HBase (row keys, column families, etc.) will be explained, along with the pros and cons of different schema decisions.
HBase In Action - Chapter 04: HBase table designphanleson
HBase In Action - Chapter 04: HBase table design
Learning HBase, Real-time Access to Your Big Data, Data Manipulation at Scale, Big Data, Text Mining, HBase, Deploying HBase
Slides from a brief talk I gave at the local JUG, javaBin. It's about our experiences using Cassandra in a production environment, with some philosophizing here and there.
Most developers are familiar with the topic of “database design”. In the relational world, normalization is the name of the game. How do things change when you’re working with a scalable, distributed, non-SQL database like HBase? This talk will cover the basics of HBase schema design at a high level and give several common patterns and examples of real-world schemas to solve interesting problems. The storage and data access architecture of HBase (row keys, column families, etc.) will be explained, along with the pros and cons of different schema decisions.
The No SQL Principles and Basic Application Of Casandra ModelRishikese MR
The slides discuss various matters of the No SQL and casandra Models, the slide gives a complete picture of the both topics and its relations. Also it discuss the merits and demerits of the topics and its features and examples are also described.
The project is focussed on Comparison Between HBASE and CASSANDRA using YCSB. It is a data storage and management project performed at National College Of Ireland
A column-oriented DBMS is a database management system (DBMS) that stores its content by column rather than by row. This has advantages for data warehouses and library catalogues where aggregates are computed over large numbers of similar data items.
In these slides we introduce Column-Oriented Stores. We deeply analyze Google BigTable. We discuss about features, data model, architecture, components and its implementation. In the second part we discuss all the major open source implementation for column-oriented databases.
by Dhanraj Pondicherry, Sr. Solutions Architecture Manager, AWS
Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze big data for a fraction of the cost of traditional data warehouses. In this session, we take an in-depth look at data warehousing with Amazon Redshift for big data analytics. We cover best practices to take advantage of Amazon Redshift's columnar technology and parallel processing capabilities to deliver high throughput and query performance. We also discuss how to design optimal schemas, load data efficiently, and use work load management. Level: 300
Chicago Data Summit: Apache HBase: An IntroductionCloudera, Inc.
Apache HBase is an open source distributed data-store capable of managing billions of rows of semi-structured data across large clusters of commodity hardware. HBase provides real-time random read-write access as well as integration with Hadoop MapReduce, Hive, and Pig for batch analysis. In this talk, Todd will provide an introduction to the capabilities and characteristics of HBase, comparing and contrasting it with traditional database systems. He will also introduce its architecture and data model, and present some example use cases.
This talk at the Percona Live MySQL Conference and Expo describes open source column stores and compares their capabilities, correctness and performance.
The No SQL Principles and Basic Application Of Casandra ModelRishikese MR
The slides discuss various matters of the No SQL and casandra Models, the slide gives a complete picture of the both topics and its relations. Also it discuss the merits and demerits of the topics and its features and examples are also described.
The project is focussed on Comparison Between HBASE and CASSANDRA using YCSB. It is a data storage and management project performed at National College Of Ireland
A column-oriented DBMS is a database management system (DBMS) that stores its content by column rather than by row. This has advantages for data warehouses and library catalogues where aggregates are computed over large numbers of similar data items.
In these slides we introduce Column-Oriented Stores. We deeply analyze Google BigTable. We discuss about features, data model, architecture, components and its implementation. In the second part we discuss all the major open source implementation for column-oriented databases.
by Dhanraj Pondicherry, Sr. Solutions Architecture Manager, AWS
Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze big data for a fraction of the cost of traditional data warehouses. In this session, we take an in-depth look at data warehousing with Amazon Redshift for big data analytics. We cover best practices to take advantage of Amazon Redshift's columnar technology and parallel processing capabilities to deliver high throughput and query performance. We also discuss how to design optimal schemas, load data efficiently, and use work load management. Level: 300
Chicago Data Summit: Apache HBase: An IntroductionCloudera, Inc.
Apache HBase is an open source distributed data-store capable of managing billions of rows of semi-structured data across large clusters of commodity hardware. HBase provides real-time random read-write access as well as integration with Hadoop MapReduce, Hive, and Pig for batch analysis. In this talk, Todd will provide an introduction to the capabilities and characteristics of HBase, comparing and contrasting it with traditional database systems. He will also introduce its architecture and data model, and present some example use cases.
This talk at the Percona Live MySQL Conference and Expo describes open source column stores and compares their capabilities, correctness and performance.
Apache Cassandra, part 1 – principles, data modelAndrey Lomakin
Aim of this presentation to provide enough information for enterprise architect to choose whether Cassandra will be project data store. Presentation describes each nuance of Cassandra architecture and ways to design data and work with them.
Learn best practices for taking advantage of Amazon Redshift's columnar technology and parallel processing capabilities to improve your data warehouse performance.
Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open-source and horizontally scalable. The original intention has been modern web-scale databases. The movement began early 2009 and is growing rapidly. Often more characteristics apply such as: schema-free, easy replication support, simple API, eventually consistent / BASE (not ACID), a huge amount of data and more. So the misleading term "nosql" (the community now translates it mostly with "not only sql") should be seen as an alias to something like the definition above.
A quick tour in 16 slides of Amazon's Redshift clustered, massively parallel database.
Find out what differentiates it from the other database products Amazon has, including SimpleDB, DynamoDB and RDS (MySQL, SQL Server and Oracle).
Learn how it stores data on disk in a columnar format and how this relates to performance and interesting compression techniques.
Contrast the difference between Redshift and a MySQL instance and discover how the clustered architecture may help to dramatically reduce query time.
Basic Introduction to Cassandra with Architecture and strategies.
with big data challenge. What is NoSQL Database.
The Big Data Challenge
The Cassandra Solution
The CAP Theorem
The Architecture of Cassandra
The Data Partition and Replication
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
2. Scope
• Introduction to Cassandra and NoSql
• Understanding Cassandra data model
• Configuration, read and writing data in Cassandra
• CQL
2
3. What is Cassandra
• A Database
• Uses Amazon’s Dyanamo’s fully distribution design
• Uses Google’s BigTable’s column family based data model
• Developed by Facebook (The team was led by Jeff Hammerbacher,
with Avinash Lakshman, Karthik Ranganathan, and Prashant Malik
(Search Team))
• Open source in 2008
3
4. Problems with RDBMS
• Horizontal scaling: In RDBMS as the size grows the joins become
slows so the retrieval become slow.
• Vertical scaling: adding more hardware, memory, faster processor or
upgrading disk space. Adding hardware creates problem like data
replication, consistency, fail over mechanism.
• Caching layer in large system: like memcache, EHCache, Oracle
Coherence. Updation in the cache and data base is exacerbated over
a cluster.
4
5. Cassandra
• Apache Cassandra is an open source, distributed, decentralized,
elastically scalable, highly available, fault-tolerant, tuneable
consistent, column-oriented database that bases its distribution
design on Amazon’s Dynamo and its data model on Google’s
Bigtable. Created at Facebook.”
5
6. Why Cassandra
• Fault tolerant
• Decentralized
• Eventually consistent
• Rich data model
• Elastic
• Highly Available
• No SPF (Single point failure)
6
7. Cap theorem
• University of California at Berkeley, Eric Brewer posted his CAP theorem in 2000.
• The theorem states that within a large-scale distributed data system, there are three
requirements that have a relationship of sliding dependency.
• Consistency: All database clients will read the same value for the same query, even given
concurrent updates.
• Availability: All database clients will always be able to read and write data.
• Partition Tolerance: The database can be split into multiple machines; it can continue functioning
in the face of network segmentation breaks.
7
8. Cap theorem (cont.)
• According to theorem only two of the three can be strongly supported distributed data
system
• CA: it means system will block when the system will partitions. so in this the system is been
limited to a single data centre to mitigate this.
• CP: it allow data sharding in order to data scaling. The data will be consistent but data may
loss whenever a node goes down.
• AP: system may return inaccurate data, but the system will always be available, even in the
face of network partitioning. DNS is perhaps the most popular example of a system that is
massively scalable, highly available, and partition-tolerant.
8
10. Fault Tolerant
• Data is automatically replicated to multiple nodes based on
replication factor.
• Replication across multiple data center
• Failed nodes can be replaced with no downtime.
• Uses Accrual Failure Detector for fault detection.
10
11. Decentralization
• Every node in the cluster is identical (No client server architecture)
• There is no single points of failure.
11
12. Eventual consistency
• Uses BASE (Basically Available Soft-state Eventual) Consistency.
• As the data is replicated, the latest version of something is sitting on
at least one node in the cluster, but old version will still be on other
node.
• Eventually all nodes will see the latest version.
12
13. Eventual consistency (Cont.)
• Tuneable Consistency: a replication factor to the number of nodes in
the cluster you want the updates to propagate to.
• Consistency level is a setting that clients must specify on every
operation and that allows you to decide how many replicas in the
cluster must acknowledge a write operation or respond to a read
operation in order to be considered successful. That’s the part where
Cassandra has pushed the decision for determining consistency out to
the client. so strict consistency can be achieved assigning same value
to replication factor and consistency level.
13
14. Rich Data Model
• Keyspace
• Column family
• Rows
• Column
• Super column
14
18. Keyspace
It is similar as we have schema in RDBMS, it contains a name and a set
of attributes that defines keyspace wide behaviour.
various attributes are:
1. Replication factor: if it is set to 3 then 3 nodes will be having
the copy of each row.
2. Replica placement strategy: like SimpleStrategy
(RackUnawareStrategy), OldNetworkTopologyStrategy (RackAwareStrategy), and NetworkTopologyStrategy (DatacenterShardStrategy).
3. Column family: will discussed.
18
19. Column family
• A column family is a container for columns, analogous to the table in
a relational system.
• A Column family holds an ordered list of columns, which is been
refered by the column name.
• [Keyspace][ColumnFamily][Key][Column]
19
20. Column family (cont.)
column family has two attributes: a name and a comparator.
comparator indicate the sorting order when they are returns against a
query. comparator can be of following types: AsciiType, BytesType,
LexicalUUIDType, IntegerType, LongType, TimeUUIDType, or UTF8Type,
Custom (plug your class to cassandra which should be extending
org.apache.cassandra.db.marshal.AbstractType)
20
21. Column family (cont.)
Hotel {
• key: AZC_043 { name: Cambria Suites Hayden, phone: 480-444-4444,
address: 400 N. Hayden Rd., city: Scottsdale, state: AZ, zip: 85255}
• key: AZS_011 { name: Clarion Scottsdale Peak, phone: 480-333-3333,
address: 3000 N. Scottsdale Rd, city: Scottsdale, state: AZ, zip: 85255}
• key: CAS_021 { name: W Hotel, phone: 415-222-2222,
address: 181 3rd Street, city: San Francisco, state: CA, zip: 94103}
• key: NYN_042 { name: Waldorf Hotel, phone: 212-555-5555,
address: 301 Park Ave, city: New York, state: NY, zip: 10019}
}
21
22. Rows
• Cassandra is column-oriented database. each row doesn’t have to
have a same number of columns (as in relational database). Each row
has a unique key, which makes it data accessible.
• Each column family is stored in a separate file.
22
23. Columns
• The column, which is a name/value pair (and a client-supplied
timestamp of when it was last updated), and a column family, which
is a container for rows that have similar, but not identical, column
sets. each column has an extra column for time stamp which records
the time when last column was last updated. rows does not have
timestamp
• columns are name/value pairs, but a regular column stores a byte
array value
23
24. Super column
• The value of a super column is a map of subcolumns (which store
byte array values).
• it’s important to keep columns that you are likely to query together in
the same column family, and a super column can be helpful for this.
• Super columns are not indexed.
• Cassandra looks like a four-dimensional hash table. But for super
columns, it becomes more like a five-dimensional hash:
[Keyspace][ColumnFamily][Key][SuperColumn][SubColumn]
24
25. Some points
• You cannot perform joins in Cassandra. If you have designed a data
model and find that you need a join, you’ll have to either do the work
on the client side, or create a denormalized second column family
that represents the join results for you.
• It is not possible to sort by value, it can only sort by column name in
order to fetch individual columns from a rows without pulling entire
row into memory.
• Column sorting is controllable, but key sorting isn’t row keys always
sort in byte order.
25
26. Elastic/Highly Avaliable
• Read and write throughput both increase linearly as new machine are
added.
• No downtime or interruption to application.
26
27. Sharding basic strategies
• feature base or functional segmentation: sharding will feature based
with no common features like user details and items for sale will be
different shards, movie rating and comments will be in different
shards.
• key based sharding: a key in data that will evenly distribute it across
shards. So instead of simply storing one letter of the alphabet for
each server as in the (naive and improper) earlier example, you use a
one-way hash on a key data element and distribute data across
machines according to the hash.
• lookup table: a table with contain information regarding the location
of the actual data.
27
28. Design Pattern
1. Materialized View (one table per query): create a secondary index to
represent the additional query. “materialized” means storing a full
copy of the original data so that everything you need to answer a
query is right there, without forcing you to look up the original data.
If you are performing a second query because you’re only storing
column names that you use, like foreign keys in the second column
family, that’s a secondary index.
28
29. Design Pattern (Cont.)
2. Valueless column: storing column value as column name. like in
user/usercity we can have city name as key and users of that city as
column names.
3. Aggregate key: key should be unique so it is possible to add two
column value with a separator to create a aggregate key.
29