What guarantee does IBM's Cloud Object Storage provide regarding data consistency / visibility? AWS's is basically read-after-write for new objects, and eventual consistency for updates/deletes. So, what is the data consistency model for IBM's COS? And, I doubt that object locking is supported, but would like to verify that it is indeed not possible (would be great if it were!).
For reference, AWS states their data consistency model here:
Amazon S3 provides read-after-write consistency for PUTS of new objects in your S3 bucket in all regions with one caveat. The caveat is that if you make a HEAD or GET request to the key name (to find if the object exists) before creating the object, Amazon S3 provides eventual consistency for read-after-write.
Amazon S3 offers eventual consistency for overwrite PUTS and DELETES in all regions.
Updates to a single key are atomic. For example, if you PUT to an existing key, a subsequent read might return the old data or the updated data, but it will never write corrupted or partial data.
and with respect to object locking:
Amazon S3 does not currently support object locking. If two PUT requests are simultaneously made to the same key, the request with the latest time stamp wins. If this is an issue, you will need to build an object-locking mechanism into your application.
Updates are key-based; there is no way to make atomic updates across keys. For example, you cannot make the update of one key dependent on the update of another key unless you design this functionality into your application.
IBM COS is immediately consistent for all operations, but like AWS doesn't support object locking at this time.
I'm working on a writeup that we'll fold into our docs about our consistency model and why it works the way it does, stay tuned!
Related
I read several blogs and watched video about usefulness of CQRS and ES. I am left with implementation confusion.
CQRS: when use separate table, one for "Write, Update and delete" and other for Read operation. So then how the data sync from write table to read table. Do we required to use cron job to sync data to read only table from write table or any other available options ?
Event Sourcing: Do we store only all Immutable sequential operation as record for each update happened upon once created in one storage. Or do we also store mutable record I mean the same record is updated in another storage
And Please explain RDBMS, NoSQL and Messaging to be used and where they fit into it
when use separate table, one for "Write, Update and delete" and other for Read operation. So then how the data sync from write table to read table.
You design an asynchronous process that understands how to transform the data from its "write" representation to its "read" representation, and you design a scheduler to decide when that asynchronous process runs.
Part of the point is that it's just plumbing, and you can choose whatever plumbing you want that satisfies your operational needs.
Event Sourcing
On the happy path, each "event stream" is a append only sequence of immutable events. In the case where you are enforcing a domain invariant over the contents of the stream, you'll normally have a "first writer wins" conflict policy.
But "the" stream is the authoritative copy of the events. There may also be non-authoritative copies (for instance, events published to a message bus). They are typically all immutable.
In some domains, where you have to worry about privacy and "the right to be forgotten", you may need affordances that allow you to remove information from a previously stored event. Depending on your design choices, you may need mutable events there.
RDBMS
For many sorts of queries, especially those which span multiple event streams, being able to describe the desired results in terms of relations makes the programming task much easier. So a common design is to have asynchronous process that read information from the event streams and update the RDBMS. The usual derived benefit is that you get low latency queries (but the data returned by those queries may be stale).
RDBMS can also be used as the core of the design of the event store / message store itself. Events are common written as blob data, with interesting metadata exposed as additional columns. The message store used by eventide-project is based on postgresql.
NoSQL
Again, can potentially be used as your cache of readable views, or as your message store, depending on your needs. Event Store would be an example of a NoSQL message store.
Messaging
Messaging is a pattern for temporal decoupling; the ability to store/retrieve messages in a stable central area affords the ability to shut down a message producer without blocking the message consumer, and vice versa. Message stores also afford some abstraction - the producer of a message doesn't necessarily know all of the consumers, and the consumer doesn't necessarily know all of the producers.
My Question is about Event Sourcing. Do we required only immutable sequence events to be stored and where to be stored ?
In event sourcing, the authoritative representation of the state is the sequence of events - your durable copy of that event sequence is the book of truth.
As for where they go? Well, that is going to depend on your architecture and storage choices. You could manage files on disk yourself, you could write them in to your own RDBMS; you could use an RDBMS designed by somebody else, you could use a NoSQL document store, you could use a dedicated message store.
There could be multiple stores -- for instance, in a micro service architecture, the service that accepts orders might be different from the service that tracks order fulfillment, and they could each be writing events into different storage appliances.
I have used MongoDB but new to Cassandra. I have worked on applications which are using MongoDB and are not very large applications. Read and Write operations are not very much intensive. MongoDB worked well for me in that scenario. Now I am building a new application(w/ some feature like Stack Overflow[voting, totals views, suggestions, comments etc.]) with lots of Concurrent write operations on the same item into the database(in future!). So according to the information, I gathered via online, MongoDB is not the best choice (but Cassandra is). But the problem I am finding in Cassandra is Picking the right data model.
Construct Models around your queries. Not around relations and
objects.
I also looked at the solution of using Mongo + Redis. Is it efficient to update Mongo database first and then updating Redis DB for all multiple write requests for the same data item?
I want to verify which one will be the best to solve this issue Mongo + redis or Cassandra?
Any help would be highly appreciated.
Picking a database is very subjective. I'd say that modern MongoDB 3.2+ using the new WiredTiger Storage Engine handles concurrency pretty well.
When selecting a distributed NoSQL (or SQL) datastore, you can generally only pick two of these three:
Consistency (all nodes see the same data at the same time)
Availability (every request receives a response about whether it succeeded or failed)
Partition tolerance (the system continues to operate despite arbitrary partitioning due to network failures)
This is called the CAP Theorem.
MongoDB has C and P, Cassandra has A and P. Cassandra is also a Column-Oriented Database, and will take a bit of a different approach to storing and retrieving data than, say, MongoDB does (which is a Document-Oriented Database). The reality is that either database should be able to scale to your needs easily. I would worry about how well the data storage and retrieval semantics fit your application's data model, and how useful the features provided are.
Deciding which database is best for your app is highly subjective, and borders on an "opinion-based question" on Stack Overflow.
Using Redis as an LRU cache is definitely a component of an effective scaling strategy. The typical model is, when reading cacheable data, to first check if the data exists in the cache (Redis), and if it does not, to query it from the database, store the result in the cache, and return it. While maybe appropriate in some cases, it's not common to just write everything to both Redis and the database. You need to figure out what's cacheable and how long each cached item should live, and either cache it at read time as I explained above, or at write time.
It only depends on what your application is for. For extensive write apps it is way better to go with Cassandra
My understanding is DynamoDB behave like a giant table which you must specify a hash key and range key.
The core concept of Google Cloud Datastore is entity based (like Cassandra) and is more flexible, i.e. can use more than 1 index.
But are there any more in-depth comparison?
AWS DynamoDB is a pretty simple flat key-value store. It has support for conditional writes and sets which allow for some cool features. You specify the amount of horsepower you want (which you can only adjust a few times a day) and AWS splits up your dataset uniformly across enough database nodes to meet your demands. You have to make sure your key values are sufficiently random as to guarantee balanced access across your dataset. AWS almost guarantees single-digit latencies. Transactions are not supported. You specify the consistency of operations.
Google Cloud Datastore is a more sophisticated key-valueish store with built-in transaction support and entity hierarchy. You don't have to worry about the capacity of the system, it automatically scales to your data size and access patterns. You have less control of some things so you have to pay attention. You cannot specify for a read to be consistent, but you can force consistency by structuring your entities in a certain way.
One downside of Google Cloud products I have experienced is that documentation and language support is not very uniform. Sometimes you have to read documentation of another language to understand the system fully and many features are not supported in certain languages.
There are a lot of other differences. Look at the API reference of your favorite language on both documentation pages and you'll get a decent feel of the specific features of each.
We have an REST-based application built on the Restlet framework which supports CRUD operations. It uses a local-file to store the data.
Now the requirement is to deploy this application on multiple VMs and any update operation in one VM needs to be propagated other application instances running on other VMs.
Our idea to solve this was to send multiple POST msgs (to all other applications) when a update operation happens in a given VM.
The assumption here is that each application has a list/URLs of all other applications.
Is there a better way to solve this?
Consistency is a deep topic, and a hard thing to get right. The trouble comes when two nearly-simultaneous changes occur to the same data: conflicting updates can arrive in one order on one server, and in another order on another. This is a problem, since the two servers no longer agree on what the data is, and it isn't clear who is "right".
The short-story: get your favorite RDBMS (for example, mysql is popular) and have your app servers connect to in what is called the three-tier model. Be sure to perform complex updates in transactions, which will provide an acceptable consistency model.
The long-story: The three-tier model serves well for small-to-medium scale web sites/services. You will eventually find that the single database becomes the bottleneck. For services whose read traffic is substantially larger than write traffic, a common optimization is to create a single-master, many-slave database replication arrangement, where all writes go to the single master (required for consistency with non-distributed transactions), but the more-common reads could go to any of the read slaves.
For services with evenly-mixed read/write traffic, you may be better served by dropped some of the conveniences (and accompanying restrictions) that formal SQL provides and instead use of one of the various "nosql" data stores that have recently emerged. Their relative merits and fitness for various problems is a deep topic in itself.
I can see 7 major options for now. You should find out more details and decide whether the facilities / trade-offs are appropriate for your purpose
Perform the CRUD operation on a common RDBMS. Simplest and most consistent
Perform the CRUD operations on a common RDBMS which runs as fast in-memory RDBMS. eg TimesTen from Oracle etc
Perform the CRUD on a distributed cache or your own home cooked distributed hash table which can guarantee synchronization eg Hazelcast/ehcache and others
Use a fast common state server like REDIS/memcached and perform your updates
in a synchronized manner on it and write out the successfull operations to a DB in a lazy manner if required.
Distribute your REST servers such that the CRUD operations on a single entity are only performed by a single master. Once this is done, the details about the changes can be communicated to everyone else using a reliable message bus or a distributed database (eg postgres) that runs underneath and syncs all of your updates fairly fast.
Target eventual consistency and use a distributed data store like Cassandra which lets you target the consistency you require
Use distributed consensus algorithms like Paxos or RAFT or an implementation of the same(recommended) like zookeeper or etcd respectively and take ownership of the item you want to change from each REST server before you perform the CRUD operation - might be a bit slow though and same stuff is what Cassandra might give you.
I'm evaluating a storage platform for an upcoming project and keep coming back to Cassandra. For this project loosing any amount of data is unacceptable. So far we've used a relational database (Microsoft SQL Server), but the data is so varied and large that it has become an issue to store and query.
Is Cassandra robust enough to use as a primary data store? Or should it only be used to mirror existing data to speed up access?
Anecdotally: yes, Twitter, Digg, Ooyala, SimpleGeo, Mahalo, and others are using or moving to Cassandra for a primary data store (http://n2.nabble.com/Cassandra-users-survey-td4040068.html).
Technically: yes; besides supporting replication (including to multiple datacenters), each Cassandra node has an fsync'd commit log to make sure writes are durable; from there writes are turned into SSTables which are immutable until compaction (which combines multiple SSTables to GC old versions). Snapshotting is supported at any time, including automatic snapshot-before-compaction.
Whether to use Cassandra for your application or not depends purely on your data workloads. Cassandra is optimised for write-intensive workloads, therefore, it is suitable for applications where a large amount of data needs to be inserted (such as infrastructure logging information at Facebook).
If however, you require fast retrievals and insertion speed is not an issue, then perhaps you should have a look at say HBase (which is optimised of read-intensive workloads).