I made my own research and found out that there is several ways to do that, but the most accurate is Change Data Capture. However, I don't see the benefits of it related to the asynchronous method for example :
Synchronous double-write: Elasticsearch is updated synchronously when
the DB is updated. This technical solution is the simplest, but it
faces the largest number of problems, including data conflicts, data
overwriting, and data loss. Make your choice carefully.
Asynchronous double-write: When the DB is updated, an MQ is recorded and used to
notify the consumer. This allows the consumer to backward query DB
data so that the data is ultimately updated to Elasticsearch. This
technical solution is highly coupled with business systems. Therefore,
you need to compile programs specific to the requirements of each
business. As a result, rapid response is not possible.
Change Data Capture (CDC): Change data is captured from the DB, pushed to an
intermediate program, and synchronously pushed to Elasticsearch by
using the logic of the intermediate program. Based on the CDC
mechanism, accurate data is returned at an extremely fast speed in
response to queries. This solution is less coupled to application
programs. Therefore, it can be abstracted and separated from business
systems, making it suitable for large-scale use. This is illustrated
in the following figure.
Alibabacloud.com
In another article it said that asynchronous is also risky if one datasource is down and we cannot easily rollback.
https://thorben-janssen.com/dual-writes/
So my question is : Should I use CDC to perform persistance operations for multiple datasources ? Why CDC is better than asynchronous given that is based on the same principle ?
Related
I am going to test multi-region write functionality by writing some test code using the cosmos c# v3 SDK.
I plan to have a multi-region write enabled cosmos DB (SQL core API) with three regions. I want to write to one specific region and then read from other regions. While doing it, I want to measure performance as well.
Is there any way of implementing these type of tests? Is there any good of measuring performance such as performance metrics? I also want to vary consistency level and see latency.
Depending on what type of tests you are looking to do the benchmarks in this Cosmos DB Global Distribution Demos GitHub Repo may be of some help. There's a bit of a learning curve as the benchmarks are data driven from app.config files. But once you get the URIs and keys in the app.config you should be mostly good to go.
One thing worth pointing out that changing consistency level when testing multiple writers and readers in different regions when configured for multi-region writes is meaningless because you will always have eventual consistency under those circumstances. For more information see, Guarantees associated with consistency levels.
The other thing to call out is you cannot configure multi-region writes with strong consistency. For more information see, Strong consistency and multiple write regions
I am currently planning to move some data sources to one place for posterior analysis.
Currently I have any data sources (databases) such as:
MSSQL
Mysql
mongodb
Postgres
Cassandra will be use for analytics in a big data pipeline. What is the best way to migrate any source to a Cassandra cluster?
I will highly recommend using NiFi for this use case. Some of benefits that I can outline right away.
Inbuilt "Processors" available for reading the data from all listed data sources and writing to Cassandra.
Very high throughput with low latency.
Rapid data acquisition pipeline development without writing a lot of code.
Ability to do "Change Data Capture" very easily later in your project, if needed.
Provides a highly concurrent model without a developer having to worry about the typical complexities of concurrency.
Is inherently asynchronous which allows for very high throughput and natural buffering even as processing and flow rates fluctuate
The resource-constrained connections make critical functions such as back-pressure and pressure release very natural and intuitive.
The points at which data enters and exits the system as well as how it flows through are well understood and easily tracked
And biggest of all, OPEN SOURCE.
You can refer Apache NiFi homepage for more information.
Hope that helps!
We are soon going to start something with GEODE regarding reference data. I would like to get some guide lines for the same.
As you know in financial reference data world there exists complex relationships between various reference data entities like Instrument, Account, Client etc. which might be available in database as 3NF.
If my queries are mostly read intensive which requires joins across
tables (2-5 tables), what's the best way to deal with the same with in
memory grid?
Case 1:
Separate regions for all tables in your database and then do a similar join using OQL as you do in database?
Even if you do so, you will have to design it with solid care that related entities are always co-located within same partition.
Modeling 1-to-many and many-many relationship using object graph?
Case 2:
If you know how your join queries look like, create a view model per join query having equi join characteristics.
Confusion:
(1) I have 1 join query requiring Employee,Department using emp.deptId = dept.deptId [OK fantastic 1 region with such view model exists]
(2) I have another join query requiring, Employee, Department, Salary, Address joins to address different requirement
So again I have to create a view model to address (2) which will contain similar Employee and Department data as (1). This may soon reach to memory threshold.
Changes in database can still be managed by event listeners, but what's the recommendations for that?
Thanks,
Dharam
I think your general question is pretty broad and there isn't just one recommended approach to cover all UCs (primarily all your analytical views/models of your data as required by your application(s)).
Such questions involve many factors, such as the size of individual data elements, the volume of data, the frequency of access or access patterns originating from the application or applications, the timely delivery of information, how accurate the data needs to be, the size of your cluster, the physical resources of each (virtual) machine, and so on. Thus, any given approach will undoubtedly require application tuning, tuning GemFire accordingly and JVM tuning regardless of your data model. Still, a carefully crafted data model can determine the extent of such tuning.
In GemFire specifically, such tuning will involve different configuration such as, but not limited to: data management policies, eviction (Overflow) and expiration (LRU, or perhaps custom) settings along with different eviction/expiration thresholds, maybe storing data in Off-Heap memory, employing different partition strategies (PartitionResolver), and so on and so forth.
For example, if your Address information is relatively static, unchanging (i.e. actual "reference" data) then you might consider storing Address data in a REPLICATE Region. Data that is written to frequently (typically "transactional" data) is better off in a PARTITION Region.
Of course, as you know, any PARTITION data (managed in separate Regions) you "join" in a query (using OQL) must be collocated. GemFire/Geode does not currently support distributed joins.
Additionally, certain nodes could host certain Regions, thus dividing your cluster into "transactional" vs. "analytical" nodes, where the analytical-based nodes are updated from CacheListeners on Regions in transactional nodes (be careful of this), or perhaps better yet, asynchronously using an AEQ with AsyncEventListeners. AEQs can be separately made highly available and durable as well. This transactional vs analytical approach is the basis for CQRS.
The size of your data is also impacted by the form in which it is stored, i.e. serialized vs. not serialized, and GemFire's proprietary serialization format (PDX) is quite optimal compared with Java Serialization. It all depends on how "portable" your data needs to be and whether you can keep your data in serialized form.
Also, you might consider how expensive it is to join the data on-the-fly. Meaning, if your are able to aggregate, transform and enrich data at runtime relatively cheaply (compute vs. memory/storage), then you might consider using GemFire's Function Execution service, bringing your logic to the data rather than the data to your logic (the fundamental basis of MapReduce).
You should know, and I am sure you are aware, GemFire is a Key-Value store, therefore mapping a complex object graph into separate Regions is not a trivial problem. Dividing objects up by references (especially many-to-many) and knowing exactly when to eagerly vs. lazily load them is an overloaded problem, especially in a distributed, replicated data store such as GemFire where consistency and availability tradeoffs exist.
There are different APIs and frameworks to simplify persistence and querying with GemFire. One of the more notable approaches is Spring Data GemFire's extension of Spring Data Commons Repository abstraction.
It also might be a matter of using the right data model for the job. If you have very complex data relationships, then perhaps creating analytical models using a graph database (such as Neo4j) would be a simpler option. Spring also provides great support for Neo4j, led by the Neo4j team.
No doubt any design choice you make will undoubtedly involve a hybrid approach. Often times the path is not clear since it really "depends" (i.e. depends on the application and data access patterns, load, all that).
But one thing is for certain, make sure you have a good cursory knowledge and understanding of the underlying data store and it' data management capabilities, particularly as it pertains to consistency and availability, beginning with this.
Note, there is also a GemFire slack channel as well as a Apache DEV mailing list you can use to reach out to the GemFire experts and community of (advanced) GemFire/Geode users if you have more specific problems as you proceed down this architectural design path.
So I've been trying hard to figure out if NoSQL is really bringing that much value outside of auto-sharding and handling UNSTRUCTURED data.
Assuming I can fit my STRUCTURED data on a single machine OR have an effective 'auto-sharding' feature for SQL, what advantages do any NoSQL options offer? I've determined the following:
Document-based (MongoDB, Couchbase, etc) - Outside of it's 'auto-sharding' capabilities, I'm having a hard time understanding where the benefit is. Linked objects are quite similar to SQL joins, while Embedded objects significantly bloat doc size and causes a challenge regarding to replication (a comment could belong to both a post AND a user, and therefore the data would be redundant). Also, loss of ACID and transactions are a big disadvantage.
Key-value based (Redis, Memcached, etc) - Serves a different use case, ideal for caching but not complex queries
Columnar (Cassandra, HBase, etc ) - Seems that the big advantage here is more how the data is stored on disk, and mostly useful for aggregations rather than general use
Graph (Neo4j, OrientDB, etc) - The most intriguing, the use of both edges and nodes makes for an interesting value-proposition, but mostly useful for highly complex relational data rather than general use.
I can see the advantages of Key-value, Columnar and Graph DBs for specific use cases (Caching, social network relationship mapping, aggregations), but can't see any reason to use something like MongoDB for STRUCTURED data outside of it's 'auto-sharding' capabilities.
If SQL has a similar 'auto-sharding' ability, would SQL be a no-brainer for structured data? Seems to me it would be, but I would like the communities opinion...
NOTE: This is in regards to a typical CRUD application like a Social Network, E-Commerce site, CMS etc.
If you're starting off on a single server, then many advantages of NoSQL go out the window. The biggest advantages to the most popular NoSQL are high availability with less down time. Eventual consistency requirements can lead to performance improvements as well. It really depends on your needs.
Document-based - If your data fits well into a handful of small buckets of data, then a document oriented database. For example, on a classifieds site we have Users, Accounts and Listings as the core data. The bulk of search and display operations are against the Listings alone. With the legacy database we have to do nearly 40 join operations to get the data for a single listing. With NoSQL it's a single query. With NoSQL we can also create indexes against nested data, again with results queried without Joins. In this case, we're actually mirroring data from SQL to MongoDB for purposes of search and display (there are other reasons), with a longer-term migration strategy being worked on now. ElasticSearch, RethinkDB and others are great databases as well. RethinkDB actually takes a very conservative approach to the data, and ElasticSearch's out of the box indexing is second to none.
Key-value store - Caching is an excellent use case here, when you are running a medium to high volume website where data is mostly read, a good caching strategy alone can get you 4-5 times the users handled by a single server. Key-value stores (RocksDB, LevelDB, Redis, etc) are also very good options for Graph data, as individual mapping can be held with subject-predicate-target values which can be very fast for graphing options over the top.
Columnar - Cassandra in particular can be used to distribute significant amounts of load for even single-value lookups. Cassandra's scaling is very linear to the number of servers in use. Great for heavy read and write scenarios. I find this less valuable for live searches, but very good when you have a VERY high load and need to distribute. It takes a lot more planning, and may well not fit your needs. You can tweak settings to suite your CAP needs, and even handle distribution to multiple data centers in the box. NOTE: Most applications do emphatically NOT need this level of use. ElasticSearch may be a better fit in most scenarios you would consider HBase/Hadoop or Cassandra for.
Graph - I'm not as familiar with graph databases, so can't comment here (beyond using a key-value store as underlying option).
Given that you then comment on MongoDB specifically vs SQL ... even if both auto-shard. PostgreSQL in particular has made a lot of strides in terms of getting unstrictured data usable (JSON/JSONB types) not to mention the power you can get from something like PLV8, it's probably the most suited to handling the types of loads you might throw at a document store with the advantages of NoSQL. Where it happens to fall down is that replication, sharding and failover are bolted on solutions not really in the box.
For small to medium loads sharding really isn't the best approach. Most scenarios are mostly read so having a replica-set where you have additional read nodes is usually better when you have 3-5 servers. MongoDB is great in this scenario, the master node is automagically elected, and failover is pretty fast. The only weirdness I've seen is when Azure went down in late 2014, and only one of the servers came up first, the other two were almost 40 minutes later. With replication any given read request can be handled in whole by a single server. Your data structures become simpler, and your chances of data loss are reduced.
Again in my own example above, for a mediums sized classifieds site, the vast majority of data belongs to a single collection... it is searched against, and displayed from that collection. With this use case a document store works much better than structured/normalized data. The way the objects are stored are much closer to their representation in the application. There's less of a cognitive disconnect and it simply works.
The fact is that SQL JOIN operations kill performance, especially when aggregating data across those joins. For a single query for a single user it's fine, even with a dozen of them. When you get to dozens of joins with thousands of simultaneous users, it starts to fall apart. At this point you have several choices...
Caching - caching is always a great approach, and the less often your data changes, the better the approach. This can be anything from a set of memcache/redis instances to using something like MongoDB, RethinkDB or ElasticSearch to hold composite records. The challenge here comes down to updating or invalidating your cached data.
Migrating - migrating your data to a data store that better represents your needs can be a good idea as well. If you need to handle massive writes, or very massive read scenarios no SQL database can keep up. You could NEVER handle the likes of Facebook or Twitter on SQL.
Something in between - As you need to scale it depends on what you are doing and where your pain points are as to what will be the best solution for a given situation. Many developers and administrators fear having data broken up into multiple places, but this is often the best answer. Does your analytical data really need to be in the same place as your core operational data? For that matter do your logins need to be tightly coupled? Are you doing a lot of correlated queries? It really depends.
Personal Opinions Ahead
For me, I like the safety net that SQL provides. Having it as the central store for core data it's my first choice. I tend to treat RDBMS's as dumb storage, I don't like being tied to a given platform. I feel that many people try to over-normalize their data. Often I will add an XML or JSON field to a table so additional pieces of data can be stored without bloating the scheme, specifically if it's unlikely to ever be queried... I'll then have properties in my objects in the application code that store in those fields. A good example may be a payment... if you are currently using one system, or multiple systems (one for CC along with Paypal, Google, Amazon etc) then the details of the transaction really don't affect your records, why create 5+ tables to store this detailed data. You can even use JSON for primary storage and have computed columns derived and persisted from that JSON for broader query capability and indexing where needed. Databases like postgresql and mysql (iirc) offer direct indexing against JSON data as well.
When data is a natural fit for a document store, I say go for it... if the vast majority of your queries are for something that fits better to a single record or collection, denormalize away. Having this as a mirror to your primary data is great.
For write-heavy data you want multiple systems in play... It depends heavily on your needs here... Do you need fast hot-query performance? Go with ElasticSearch. Do you need absolute massive horizontal scale, HBase or Cassandra.
The key take away here is not to be afraid to mix it up... there really isn't a one size fits all. As an aside, I feel that if PostgreSQL comes up with a good in the box (for the open-source version) solution for even just replication and automated fail-over they're in a much better position than most at that point.
I didn't really get into, but feel I should mention that there are a number of SaaS solutions and other providers that offer hybrid SQL systems. You can develop against MySQL/MariaDB locally and deploy to a system with SQL on top of a distributed storage cluster. I still feel that HBase or ElasticSearch are better for logging and analitical data, but the SQL on top solutions are also compelling.
More: http://www.mongodb.com/nosql-explained
Schema-less storage (or schema-free). Ability to modify the storage (basically add new fields to records) without having to modify the storage 'declared' schema. RDBMSs require the explicit declaration of said 'fields' and require explicit modifications to the schema before a new 'field' is saved. A schema-free storage engine allows for fast application changes, just modify the app code to save the extra fields, or rename the fields, or drop fields and be done.
Traditional RDBMS folk consider the schema-free a disadvantage because they argue that on the long run one needs to query the storage and handling the heterogeneous records (some have some fields, some have other fields) makes it difficult to handle. But for a start-up the schema-free is overwhelmingly alluring, as fast iteration and time-to-market is all that matter (and often rightly so).
You asked us to assume that either the data can fit on a single machine, OR your database has an effective auto-sharding feature.
Going with the assumption that your SQL data has an auto-sharding feature, that means you're talking about running a cluster. Any time you're running a cluster of machines you have to worry about fault-tolerance.
For example, let's say you're using the simplest approach of sharding your data by application function, and are storing all of your user account data on server A and your product catalog on server B.
Is it acceptable to your business if server A goes down and none of your users can login?
Is it acceptable to your business if server B goes down and no one can buy things?
If not, you need to worry about setting up data replication and high-availability failover. Doable, but not pleasant or easy for SQL databases. Other types of sharding strategies (key, lookup service, etc) have the same challenges.
Many NoSQL databases will automatically handle replication and failovers. Some will do it out of the box, with very little configuration. That's a huge benefit from an operational point of view.
Full disclosure: I'm an engineer at FoundationDB, a NoSQL database that automatically handles sharding, replication, and fail-over with very little configuration. It also has a SQL layer so you you don't have to give up structured data.
I was recently given the task of rebuilding an existing RIA. The new RIA that I've designed is based on Silverlight, with a WCF service to connect to MS SQL Server. This is my first time doing something like this, so I'm not sure how to design the entire thing.
Basically, the client can look through graphs of "stocks" (allowing the client to choose different time periods, settings, etc). I've written the whole application essentially, but I'm not sure how to put it together.
The graphs are supposed to be directly based on the database, and to create the datapoints on the graph, some calculations need to be done (not very expensive ones).
The problem I'm having is to decide where to put the calculations (client or serverside? Or half and half?)
What factors should I look for to help me decide where the calculations should be done? And how can I go about optimizing this (caching, etc)?
Obviously this is a very broad subject, so I'm not expecting an immediate answer, but any help/pointing in the right direction/resources would be appreciated.
A few tips for this kind of app.
Put as much logic as possible on the client.
Make the client responsible for session data, making all your server code stateless.
Try to minimize traffic to and from the server (Bigger requests are more efficient than multiple smaller ones) so consolidate requests when possible.
If this project is likely to grow beyond it's current feature set I think it's probably a good idea to perform the calculations client side. This can avoid scaling issues, because you're using all the client side CPUs ratther than you're single, precious server CPU. This does however rely on being able to transfer the required data to the client in an efficient way, otherwise you replace a processor bottleneck with a network bottleneck.
As for caching it depends on your inputs, what variables can users of the client affect? If any of the variables they can alter are discrete (ie they can be a fixed set of values) then they're candidates for caching. For example if a user can select a date range of stock variations to view then that's probably not so useful, if however they can only select a year then you could cache your data sets by year (download each data set to the client and perform your calculation). I'd not worry about caching too much unless you find it's a real performance problem, it'll only make your code more complex, so don't add it until you have proven you need it.
One other thing, if this project is unlikely to be a long term concern then implement the calculations wherever is easiest and fastest, you can revisit if the project becomes more important later on.
Be REALLY REALLY careful about implementing client-side caching. Caching is INSANELY hard to do right while maintaining performance, security and correctness. Note that your DB Server's caching mechanism is already likely to be way better than any local caching mechanism you're likely to implement in less than 2 weeks' effort!
I would urge you to do as much work on the back-end as possible and to limit your client to render the data in a manner that is appropriate for your users. While many may balk at this suggestion, it's based on a number of observations from building many such systems in the past:
If you're going to filter some of the data returned by your service, you've just wasted thousands of clock cycles shipping data that need never have left your server
If you're going to sort your data, your DB could have done the sorting for you (often using otherwise idle CPU ticks) while waiting for the data to be read from its disks.
Your server most likely has more CPU and RAM available than your clients and has a surprising amount of "free time" to use for sorting, filtering, running inline calculations, etc., while its waiting for disks to read sectors etc.
As Roman suggested: Minimize your round-trips between your client and your server as much as possible.
But perhaps most importantly:
BEFORE YOU START DESIGNING YOUR SYSTEM, state your performance goals
Design what you think will achieve those goals. Try to find bottlenecks in your design, particularly areas where you make blocking calls. Re-design those areas to use async patterns wherever you can.
Build your intended solution
Measure your actual perforamnce under actual real-world load
If you're within your expected performance goals, then you're done.
If not, work out where you're spending too long and tune the design of that portion of the system. Goto 3.
Don't try to build the perfect system in one try - chances are that you won't manage it, no matter how hard you try, for a variety of reasons including user expectations, your servers ability to process the required load, your clients' ability to handle the returned data, your network's ability to carry the traffic, etc.
They're a little old now, but I suggest you read through some of the earlier posts at http://blogs.msdn.com/richardt for more thoughts around designing and constructing Service Oriented and distributed systems.