![References
[Özsu & Valduriez 2020] Tamer Özsu, Patrick
Valduriez.
Principles of Distributed Database Systems, 4th
Edition.
Springer, 2020.](https://image.slidesharecdn.com/1-210326161924/85/Understanding-Distributed-Databases-Scalability-18-320.jpg)
Understanding Distributed Databases Scalability
- 1. Performance Also an overloaded term with different meanings depending on the context See definition next Scalability An overloaded term that has been perverted by technical marketing The ability of a database to improve performance when adding more resources Scalability & Performance 1
- 3. 3 S C A L A B I L I T Y & P E R F O R M A N C E Throughput: The number of operations per time unit (e.g., transactions per second, operations per second, queries per second) Response time: The time from submitting an operation (e.g., transaction, query, individual row operation) until receiving the answer Database Performance Metrics
- 4. 4 S C A L A B I L I T Y & P E R F O R M A N C E The ability of a database to deliver better performance by adding more resources Scalability
- 5. 5 S C A L A B I L I T Y & P E R F O R M A N C E The ability of a database to reduce response time by adding more resources Speedup
- 6. Adding more resources (CPU, memory and disk) to a centralized database yields more throughput Adding more nodes to a distributed database (in a cluster) yields more throughput Vertical vs Horizontal Scalability
- 7. Do all databases scale the same? Scalability Factor 7 Can we measure and compare scalabilities? Measures scalability: scale up for vertical scalability and scale out for horizontal scalability
- 8. Scalability Factor The scale out factor provides the throughput of a cluster size normalized to the relative throughput of a single node It can also be defined as the ratio between the throughputs of a database with one node and a database with n cluster nodes
- 9. What is the optimal scalability? Types of Scalability 9 What is the worst scalability? Scalability can be logarithmic or linear, but can be also null or even negative
- 10. Types of Scalability Some databases have negative scalability, as adding more nodes to the system yields a throughput lower than with a single node Many databases have sublinear scalability Often, scalability is null for write workloads and logarithmic for read/write workloads Linear scalability is the optimal case: with a cluster of n nodes, you get n times the throughput of a single node For instance, if a single node delivers 1,000 transactions per second, a cluster of 100 nodes delivers a throughput of 100,000 transactions per second
- 11. Logarithmic Scale Out Results from wasting capacity due to redundant work and/or contention Open source databases such as MariaDB rely on cluster replication (see our blog post on Cluster Replication) Cluster replication yields logarithmic scalability: since the writes are executed by all nodes, only the read fraction of the workload can provide scalability Shared disk databases also have logarithmic scalability: the need for a concurrency control protocol that locks disk pages to be written results in substantial contention that increases with the cluster size T Y P E S O F S C A L A B I L I T Y
- 12. Linear Scalability Key-value stores (see our blog post on NoSQL) typically provide linear scalability because they are very simple, without addressing the hard problem of scaling transactional management (the so-called ACID properties) Transactional databases that exhibit linear scalability are very few (but since this blog series is vendor agnostic, we don't discuss them) T Y P E S O F S C A L A B I L I T Y
- 13. Types of Speed Up Speed up can also show different behaviors, from null to linear Linear speed up means that the response time obtained with one node is divided by n with n nodes Null speed up means, for instance, that a given query always exhibit the same response time with one or more nodes Null speed up happens in a database without a parallel/OLAP query engine (i.e., without intra-query parallelism): with inter-query parallelism only, each node is able to process a subset of the queries, but each query can only be executed by a single node
- 14. The two main metrics for measuring the performance of a database are throughput and response time Throughput measures the number of operations (transactions, queries, inserts) per unit of time Response time measures how long it takes to execute a particular operation 14 2 1 3 Main Takeaways
- 15. Scalability is the ability of the database to handle bigger loads with more resources In a distributed database, we talk about horizontal scalability where more resources mean more nodes In a centralized database, we talk about vertical scalability where more resources mean more CPU, memory, and disk 15 2 2 3 Main Takeaways
- 16. Speed up is related to scalability but a different concept Refers to the ability of reducing response time by adding more resources Again, can be horizontal for a distributed database or vertical for a centralized database 16 3 2 3 Main Takeaways
- 17. Scalability and speed up can be of different kinds Negative and null are of no interest Logarithmic scalability can be better but only for a few nodes and high proportion of reads 17 4 Linear scalability is optimal since each new node contributes the same in terms of additional load that can be handled 2 3 Main Takeaways
- 18. References [Özsu & Valduriez 2020] Tamer Özsu, Patrick Valduriez. Principles of Distributed Database Systems, 4th Edition. Springer, 2020.
- 19. Relevant Posts from the Blog How To Measure Scalability and Performance Cluster Replication Shared Nothing Architectures NoSQL
- 20. About About the authors: Dr. Ricardo Jimenez-Peris is the CEO and founder of LeanXcale. Before founding LeanXcale, he was for over 25 years a researcher in distributed database systems, published over 100 scientific publications and has been director of the Distributed Systems Lab and university professor on distributed systems. Dr. Patrick Valduriez is a researcher at INRIA, co-author of the book “Principles of Distributed Database Systems” that has educated legions of students and engineers in this field and more recently, Scientific Advisor of LeanXcale. About this blog series: This blog series aims at educating database practitioners in topics commonly not well understood, often due to false or confusing marketing messages. The blog provides the foundations and tools to let the reader actually evaluate database systems, learn their real capabilities and be able to compare the performance of the different alternatives for its targeted workload. The blog is vendor agnostic and does not mention specific vendors, sometimes open source databases are mentioned to illustrate concepts. About LeanXcale: LeanXcale is a startup making a NewSQL database. Since the blog is vendor agnostic, we do not talk about LeanXcale itself. Readers interested on LeanXcale can visit LeanXcale web site.