7 Must-know Strategies to Scale Your Database
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- Опубликовано: 30 июн 2024
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Table Partitioning:
If a single table is too large for the index, it may be a good idea to split the table (for example) per month of data. Your queries now must specify the month to select the right table(s), but each table gets its own index, instead of having one massive index, returning speed to normal.
It doesn't have to be per month - you can also select per year or day, and it doesn't have to be per date either: You could split it per category or name as well.
aka- Archiving data unlikely to be retrieved during normal operations, if it significantly reduces rowcount.
Isn't that sharding?
@@raptyaxa5771partitions stay on the same server
@@raptyaxa5771 Shards are separate servers.
This video dropped when I was considering researching this topic. Awesome !!
Thanks! That was informative
Thank you Alex. wonderful video. Additionally, I believe elastic search is good option as well for scaling reads...if eventual consistency between primary db and elastic search be maintained
Always informative. Thanks
Thank you. Very informative 👏
You have to give us another tutorial to create those slick animations, please :). Great video btw
Thank you!
this,
is
amazing
thank you
Nice succinct summary:)
Keep up the Good work
thanks for another excellent video....every time I see such videos to make the applications faster, I just wonder that are we pushing the hardware/software limits or we are reducing our patience & sanity limits.
I would like to have a big enterprise to assign this man as my CTO
Nice tut 🎉🎉
Thanks for this great video, could you share which software or framework you use to create the animations?
Very nice
Hello Sir!!, big fan of your content, very knowledgeable, a quick question what tool do you use to simulate all the workflow diagrams for understanding considering all architectural diagrams
Data archival is also an excellent technique when the system scales and table size increases apart from partitioning as discussed in some other comments as it decreases overall load on the system. It's better to shard the db, partition it, and archive the older partitions.
Thanks
Very useful information. Can I ask what tool you use for design diagrams? Thanks
golden!!
Wat abt partitioning of tables eg in postgres, tey help immensely
hello bytebytego can you make a series of videos explaining reverse engineering web APIs, automation
Your animations are amazing!
I must say , but content is very shallow no depth at all
I want to Ask you what IS thé softwar you use to create this beautifull presentation
What software do you use to make the video?
Is it even legal to make content this good!?
My god, this guy
Note to self, time stamps are:
1) Indexing: 0:58
2) Materialised Views: 2:06
3) Denormalisation 3:04
4) Vertical Scaling: 3:47
5) Database Caching: 5:00
6) Replication 6:01
7) Sharding 6:58
🎯 Key points for quick navigation:
00:00 *📈 Scaling database importance*
- Why scaling is necessary as applications grow
- Effects of increased load on performance
- Importance of maintaining smooth operations for good user experience
01:09 *📊 Indexing for database performance*
- Indexes help locate information quickly in a database
- B+ tree indexes are common and efficient for various queries
- Balancing indexing for improved performance without slowing down write operations
02:07 *📑 Materialized views benefits and considerations*
- Materialized views store pre-computed data for faster access
- Balancing data refresh frequency with performance benefits is essential
- Efficiency gains from materialized views in complex query scenarios
03:01 *⬆️ Denormalization advantages and drawbacks*
- Denormalization simplifies data retrieval and speeds up queries
- Consistency challenges with managing redundant data during updates
- Impact of denormalization on complex query executions
03:56 *💻 Vertical scaling for immediate performance improvement*
- Adding resources to an existing database server to handle increased load
- Addressing limitations and cost considerations of vertical scaling
- The importance of redundant database configuration in vertical scaling
05:04 *🚀 Caching to reduce database load and improve response times*
- Storing frequently accessed data in a faster storage layer
- Addressing cache invalidation challenges for maintaining data accuracy
- Implementing caching at various levels for improved performance
06:14 *🔄 Database replication for availability and fault tolerance*
- Creating copies of primary databases on different servers
- Configuring synchronous and asynchronous replication for data consistency
- Challenges of managing data consistency and overhead with replication
07:08 *🔀 Sharding for efficient distribution of database workload*
- Splitting a large database into smaller, manageable pieces called shards
- Effective scalability by distributing workload across multiple servers
- Challenges and benefits of horizontal scaling through sharding techniques
Made with HARPA AI
Is it possible to combine two or more strategies?
yes
Regarding the strategies of this video, you can do all of them at the same time.
7 Ineffable Paradigms for Augmenting Database Scalability
**1. Indexation: The Cryptic Codex of Data Retrieval**
Analogous to the esoteric indices of an arcane grimoire, database indices facilitate the expeditious exhumation of information sans the necessity of scrutinizing every infinitesimal datum.
* **Modus Operandi:** Indices constitute labyrinthine data structures that warehouse specific column values and indicate the corresponding rows within the tabular labyrinth.
* **Exemplification:** In a repository of clientele, indexing the 'customer identifier' permits swift chronological excavation of transactional history, circumventing a comprehensive perusal of the tabular expanse.
* **Taxonomies:**
* **B-tree Index:** The most ubiquitous typology, suitable for a wide spectrum of inquiries, including range-bound interrogations. Proffers rapid insertion, deletion, and lookup operations within its arboreal structure.
* **Advantages:** Substantially diminishes the temporal duration required for query execution.
* **Disadvantages:**
* May decelerate write operations as the index necessitates perpetual recalibration with each infinitesimal data transmutation.
* Ascertaining the optimal equilibrium and selecting appropriate fields for indexation is a Herculean task crucial for peak performance.
**2. Materialized Views: Platonic Ideals of Pre-computed Data**
Materialized views constitute the pre-calculated quintessence of intricate queries, warehoused in a realm of expeditious access.
* **Modus Operandi:** A materialized view entombs the query outcome, undergoing periodic metamorphosis to reflect the most recent data transmutations.
* **Exemplification:** In a Business Intelligence pantheon, a materialized view can enshrine daily sales reports, generated from a voluminous dataset of Cyclopean proportions. In lieu of perpetually interrogating the entire dataset, the report can be instantaneously manifested from the view's crystallized form.
* **Advantages:** Substantially enhances performance by mitigating the Sisyphean computational burden.
* **Disadvantages:**
* Necessitates periodic rejuvenation to maintain data congruence, a process of Promethean resource consumption.
* Striking a Delphic balance between refresh frequency and performance benefits is crucial.
**3. Denormalization: The Faustian Bargain of Data Redundancy**
Denormalization entails the introduction of deliberate redundancy, a Mephistophelian pact of warehousing data in multiple loci to accelerate retrieval.
* **Modus Operandi:** Redundant data is appended to tables with Borgesian duplication, circumventing the need for complex joins across multiple tabular realms.
* **Exemplification:** Social media Leviathans frequently denormalize data to warehouse user posts and information within the same tabular expanse, expediting the Herculean task of feed generation.
* **Advantages:** Substantially augments read performance by simplifying the labyrinthine process of query execution.
* **Disadvantages:**
* Augments storage requisites with Brobdingnagian voracity.
* Necessitates meticulous, Sisyphean management of updates to maintain congruence across the database's multifarious facets.
* Can engender complexities and potential issues of Gordian proportions if not handled with Solomonic wisdom.
**4. Vertical Scaling: Promethean Augmentation of Silicon Titans**
Vertical scaling, or "scaling up," involves the Titanic augmentation of resources to your extant database server, a process akin to bestowing godlike powers upon mortal silicon.
* **Modus Operandi:** Upgrading hardware such as CPU, RAM, or storage capacity of the existing server to Olympian proportions.
* **Exemplification:** An online marketplace experiencing Promethean growth upgrades its database server to contend with increased load and transaction volume of Biblical proportions.
* **Advantages:**
* Relatively straightforward to implement, akin to granting Herculean strength to Atlas.
* Provides immediate performance enhancements without necessitating Daedalian modifications to application architecture.
* **Disadvantages:**
* Limited scalability due to the Procrustean constraints of hardware limitations and pecuniary considerations.
* Fails to address redundancy; a single server failure can still precipitate a database apocalypse.
**5. Caching: The Mnemosyne's Embrace of Ephemeral Data**
Caching, the art of storing frequently accessed data in Mnemosyne's bosom, provides a stratum of expeditious retrieval and mitigated database encumbrance.
* **Modus Operandi:** Frequently accessed data is ensconced in a cache (in-memory or application-level), diminishing the Sisyphean necessity to perpetually query the database.
* **Exemplification:** A streaming service of Amazonian proportions caches movie metadata to expedite title display, mitigating the Herculean database load.
* **Advantages:**
* Drastically reduces response times for frequently accessed data to near-instantaneous levels.
* Enhances user experience to heights of Elysian bliss.
* **Disadvantages:**
* Necessitates a cache invalidation strategy of Delphic complexity to ensure data congruence.
* Stale cache data can lead to the Cassandra-like prophecy of inaccurate information.
**6. Replication: The Hydra-headed Proliferation of Data Simulacra**
Replication involves the creation of Hydra-like copies of the primary database on disparate servers to enhance availability, distribute load with Herculean efficiency, and augment fault tolerance to Olympian levels.
* **Modus Operandi:** Data undergoes mitotic division from the primary database to replica servers.
* **Taxonomies:**
* **Synchronous Replication:** Ensures immediate data congruence but introduces latency akin to Achilles' pursuit of the tortoise.
* **Asynchronous Replication:** Proffers superior performance but may engender temporary incongruities, a Schrödinger's cat of data states.
* **Advantages:**
* Enhances read performance and availability to near-omniscient levels.
* Augments fault tolerance with Promethean resilience.
* **Disadvantages:**
* Augments storage and maintenance overhead to Brobdingnagian proportions.
* Introduces complexity in maintaining data congruence, particularly in distributed systems of Borgesian intricacy.
**7. Sharding: The Alexandrian Solution to the Gordian Knot of Data Magnitude**
Sharding involves the bifurcation of a voluminous database into smaller, more manageable segments called shards, distributed across multiple servers with Alexandrian precision.
* **Modus Operandi:** Each shard contains a subset of the data predicated on a specific sharding key, akin to dividing the world among Olympian deities.
* **Exemplification:** Instagram, that Panopticon of digital narcissism, shards its database by user ID, distributing data across a multitude of servers with the efficiency of Daedalus's labyrinth, achieving a load balancing feat worthy of Atlas himself.
* **Advantages:**
* Permits horizontal scaling by appending more servers, akin to adding new realms to Yggdrasil.
* Substantially enhances both read and write performance to near-lightspeed efficiency.
* **Disadvantages:**
* Introduces complexity in database design and management that would perplex even the Sphinx.
* Selecting the appropriate sharding key is a task of Delphic importance, crucial for equitable data distribution.
* Querying and re-sharding can be complex and resource-intensive, a Herculean labor that would make Sisyphus weep.
Sounds more like database scaling strategies by Socrates, Plato, Aristoteles et al. 😊
What about partitioning ?
That's shardinng
Just stick all the data on a single big HD. Problem solved! 😋😋
But the scaling problem will occur will become single point of failure
@@yatinarora1252 then, you can focus only on that single point of failure.
Single big High definition ? I'm not sure if all my data can fit into a 1280x720 ...
You must be trolling lol
vhai tora namaj, porda thik moto korisss
建议不要露脸，没必要
Keep up the Good work