Why are clustered SQL RDBMS not efficient?
They are not inherently less efficient than NoSQL databases because the (possible) performance bottlenecks are introduced by things NoSQL (sometimes) lacks (like joins and where restrictions) which you can opt not to use. Clustered SQL RDBMS's can scale reads by introducing additional nodes in the cluster.
What is consistency constraint?
Consistency constraints mean that all nodes in the cluster must be identical. If you write to one node, this write must be copied to all other nodes before returning a response to the client. This makes a traditional RDBMS cluster hard to scale.
Which system should be able to support very large databases with very high request rates at very low latency?
Scalability is the system that should be able to support very large databases with very high request rates at very low latency.
Do noSQL databases have all that?
noSQL databases don't have all that. If you need that stuff, you need to do it yourself, but if you DON'T need it (and there are a lot of applications that don't), then boy howdy are you in luck. The DB doesn't have to do all of these complex operations and locking across much of the dataset, so it's really easy to partition the thing across many servers/disks/whatever and have it work really fast.
How many reputations do you need to answer a highly active question?
Highly active question. Earn 10 reputation (not counting the association bonus) in order to answer this question. The reputation requirement helps protect this question from spam and non-answer activity.
What is write scaling?
Write scaling is where things get hairy. There are various constraints imposed by the ACID principle which you do not see in eventually-consistent (BASE) architectures:
Is NoSQL a RDBMS?
The NoSQL databases have been adding features from the RDBMS world like SQL API's and transaction support. There are now even databases which promise SQL, ACID and write scaling, like Google Cloud Spanner, YugabyteDB or CockroachDB. Typically the devil is in the details, but for most purposes these are "ACID enough".
How does sharding work in MongoDB?
MongoDB uses sharding to support deployments with very large data sets and high throughput operations. Database systems with large data sets or high throughput applications can challenge the capacity of a single server. For example, high query rates can exhaust the CPU capacity of the server. Working set sizes larger than the system's RAM stress the I/O capacity of disk drives. There are two methods for addressing system growth: vertical and horizontal scaling. Vertical Scaling involves increasing the capacity of a single server, such as using a more powerful CPU, adding more RAM, or increasing the amount of storage space. Limitations in available technology may restrict a single machine from being sufficiently powerful for a given workload. Additionally, Cloud-based providers have hard ceilings based on available hardware configurations. As a result, there is a practical maximum for vertical scaling. Horizontal Scaling involves dividing the system dataset and load over multiple servers, adding additional servers to increase capacity as required. While the overall speed or capacity of a single machine may not be high, each machine handles a subset of the overall workload, potentially providing better efficiency than a single high-speed high-capacity server. Expanding the capacity of the deployment only requires adding additional servers as needed, which can be a lower overall cost than high-end hardware for a single machine. The trade off is increased complexity in infrastructure and maintenance for the deployment. MongoDB supports horizontal scaling through sharding . Sharded Cluster ¶ A MongoDB sharded cluster consists of the following components: shard : Each shard contains a subset of the sharded data. Each shard can be deployed as a replica set . mongos : The mongos acts as a query router, providing an interface between client applications and the sharded cluster. Starting in MongoDB 4.4, mongos can support hedged reads to minimize latencies. config servers : Config servers store metadata and configuration settings for the cluster. The following graphic describes the interaction of components within a sharded cluster: MongoDB shards data at the collection level, distributing the collection data across the shards in the cluster. Shard Keys ¶ MongoDB uses the shard key to distribute the collection's documents across shards. The shard key consists of a field or multiple fields in the documents. Starting in version 4.4, documents in sharded collections can be missing the shard key fields. Missing shard key fields are treated as having null values when distributing the documents across shards but not when routing queries. For more information, see Missing Shard Key Fields . In version 4.2 and earlier, shard key fields must exist in every document for a sharded collection. You select the shard key when sharding a collection . Starting in MongoDB 5.0, you can reshard a collection
How many orders of magnitude does a linear system grow linearly?
In the end we had a nicely scaled out system that grew linearly in performance by two orders of magnitude without too much pain.
What is a replication?
Replication - The use of events, transaction logs, state copy, or other means to provide (i) resiliency to server failure, (ii) dynamic repartitioning, (iii) immediate access to non-mutating state, and (iv) local cache copies of mutating state.
What is DataGrip?
DataGrip, a powerful GUI tool for SQL.
Which SQL Server can you configure?
SQL Server and Oracle will let you do this, it’s just more complicated to configure.
Does SQL have a replication problem?
Where with normalized RDBMS databases increase complexity with JOIN’s and speed performance costs as data increases, No SQL doesn’t have these problems, though with No SQL replication of data is on an as needed basis which can take up a lot of st
Is NDB cluster a database?
There are implementations of sharded distributed databases, some of which propietary (Google F1), some of which open source and available - Hive and Impala are declarative interfaces with write access to Hadoop based storages, NDB cluster is an actual distributed database with more or less standard SQL access and a few more products exist. They do exist and are popular, because nobody actually wants to write code that pushes a cursor through an index and discards records procedurally - this is slow, boring and error prone. We have code generators for that, and we call them planners and optimizers for HQL or SQL or similar declarative languages.
What is horizontal scaling?
Horizontal Scaling. Horizontal Scaling is essentially building out instead of up. You don't go and buy a bigger beefier server and move all of your load onto it, instead you buy 1+ additional servers and distribute your load across them. Horizontal scaling is used when you have the ability to run multiple instances on servers simultaneously.
Why is horizontal scaling important?
Horizontal scaling is great when you need an application that needs to handle a high amount of writes and parallel reads. Think of a website that is getting a huge amount of traffic and needs to log this, or needs to log a large number of events.
What does "no join" mean in SQL?
Make it simpler to understand. NO SQL DB means no JOIN which means your number of columns stays the same that means all you care about scaling is the number of data i.e. the number of rows which means horizontal growth or scaling.
Why do RDBMSs fail?
Traditional RDBMSs fail here for a variety of reasons (including the fact that they are ACID), and NoSQL solutions excel here because they can be easily scaled horizontally - but that's another story (RDBMS/SQL vs NoSQL.)
What is the meaning of "back up"?
Making statements based on opinion; back them up with references or personal experience.
Can you do horizontal scaling with commodity hardware?
I guess the biggest plus point is that horizontal scaling can be done using commodity hardware, and there is only so much you can do with vertical scaling and trying to make your existing boxes bigger.
Can a relational database run parallel?
Relational Databases are one of the more difficult items to run full read/write in parallel.
