I'm building a B2B Node app which has heavily related data models. We currently have our own search queries, but as we scale some of the queries appear to be becoming sluggish.
We will need to support multilingual search as well as content-based searches (searching matching content within related data).
The queries are growing more and more complicated (each has multiple joins on joins on joins) and I'm now considering a hosted search tool such as Algolia.
Given my concerns below, why should I use a hosted cloud search service rather than continue building my own queries?
Data privacy is important
Data is hosted in our own postgres DB - integrations with that are important (e.g.: will I now need to manually maintain our DB data and data in Algolia?)
Speed will be important, but not so much now
Must be able to do content-based searches across multiple languages
We are a tiny team of devs now, so dev resource time is vital
What other things should I be concerned about that can help make a decision in search capabilities?
Regarding maintenance of both DB and Cloud data, it seems it's as simple as getting all data, caching it, and storing it in the cloud:
var index = Algolia.initIndex('contacts');
var contactsJSON = require('./contacts.json');
index.addObjects(contactsJSON, function(err, content) {
if (err) {
console.error(err);
}
});
Search services like Algolia or self-hosted Elasticsearch/solr operate as full text search, not relational db queries.
But it sounds like the bottleneck is the continual rejoining. Which if you can make your relational data act like a full text document db then that could be a more efficient type of index (pre-joined sort of).
You might also look into views, or a data warehouse (maybe star schema).
But if you are going the search route maybe investigate hosting your own elasticsearch.
You could specify database, schema, sql, index, query details if you want more help.
Full Disclosure: I founded a company called SearchStax on the premise that companies and developers should not spend time setting up, managing, scaling or building tools for the search infrastructure (ops) - they are better off investing time of their employees into building value for the company, whether that be features, capabilities, product or customers.
Open Source Search solutions based on top of Lucene (Apache Solr / Elasticsearch) have what you need now and what you might need in near future from a capability perspective from a search engine. Find a mature service provider / AS-A-Service company that has specialization in open source search and let them deal with all. It may look small effort right now, though it's probably not worth time and effort of your devs to spend time on the operations of that.
For your concerns mentioned above:
Data privacy is important
Your concern around Privacy and Security are addressable. There are multiple ways you can secure your Solr environment and the right MSP or a Managed Solution provider should be able to address those.
a. Security at the transport layer can be addressed by SSL certificates. All the data going over the wire is encrypted.
b. IP Filtering and User Based Authentication should address who has access to what. Solr-as-a-Service offering by Measured Search supports both.
c. Security at rest can be addressed in multiple ways - OS level / File encryption, but you can even go further by ensuring not even your services provider has access to that data by using Searchable Encryption technology.
Privacy concerns are all address by Terms & Conditions - I am sure your legal department will address that from a Service Provider's perspective.
Data is hosted in our own postgres DB - integrations with that are important
Solr provides ability to import data directly (DIH) through a traditional relational database (MySQL, Postgres, Oracle, etc). You can either use that so Solr can pull data periodically or write your own simple script to push data through the Solr APIs.
If you are hosted in the cloud (AWS), a tunnel can be created so only the Solr deployments have the ability to pull data from your servers and your database servers are not exposed to the world, if you choose to go the DIH route.
Speed will be important, but not so much now
Solr is built for search speed - I don't think that's where your problems are going to be. Service offering like Measured Search's - you can spin up a cluster in any data center supported by AWS or Azure and make sure your search deployments are closer to your application servers so the latency overhead is minimal.
Must be able to do content-based searches across multiple languages
Yes, Solr supports that. More than 30 languages.
We are a tiny team of devs now, so dev resource time is vital
I am biased here, but I would not have my developers spend much time on operations and let them focus on what they do best - build great product capabilities to push the limits and deliver business value.
If you are interested in doing a comparison and ROI of doing it yourself vs using a solr-as-a-service like offered by SearchStax, check this paper out - https://www.searchstax.com/white-papers/why-measured-search-is-better-than-diy-solr-infrastructure/
Related
We are currently working on a design using Azure functions with Azure storage queue binding.
Each message in the queue represents a complete transaction. An Azure function will be bound to that queue so that the function will be triggered as soon as there is a new message in the queue.
The function will then commit the transaction in a SQL DB.
The first-cut implementation is also complete; and it's working fine. However, on retrospective, we are considering the following:
In a typical DAL, there are well-established design patterns using entity framework, repository patterns, etc. However, we didn't find a similar guidance/best practices when implementing DAL within a server-less code.
Therefore, my question is: should such patterns be implemented with Azure functions (this would be challenging :) ), or should the server-less code be kept as light as possible or this is not a use-case for azure functions, at all?
It doesn't take anything too special. We're using a routine set of library DLLs for all kinds of things -- database, interacting with other parts of Azure (like retrieving Key Vault secrets for connection strings), parsing file uploads, business rules, and so on. The libraries are targeting netstandard20 so we can more easily migrate to Functions v2 when the right triggers become available.
Mainly just design your libraries so they're highly modularized, so you can minimize how much you load to get the job done (assuming reuse in other areas of the system is important, which it usually is).
It would be easier if dependency injection was available today. See this for a few ways some of us have hacked it together until we get official DI support. (DI is on the roadmap for Functions, I believe the 3.0 release.)
At first I was a little worried about startup time with the library approach, but the underlying WebJobs stack itself is already pretty heavy, and Functions startup performance seems to vary wildly anyway (on the cheaper tiers, at least). During testing, one of our infrequently-executed Functions has varied from just ~300ms to a peak of about ~3800ms to parse the exact same test file, with all but ~55ms spent on startup).
should such patterns be implemented with Azure functions (this would
be challenging :) ), or should the server-less code be kept as light
as possible or this is not a use-case for azure functions, at all?
My answer is NO.
There should be patterns to follow, but the traditional repository patterns and CRUD operations do not seem to be valid in the cloud era.
Many strong concepts we were raised up to adhere to, became invalid these days.
Denormalizing the data base became something not only acceptable but preferable.
Now designing a pattern will depend on the database you selected for your solution and also depends of the type of your application and the type of your data.
This is a link for general guideline when you do Table Storage design Guidelines.
Is your application read-heavy or write-heavy ? The design will vary accordingly.
Are you using Azure Tables or Mongo? There are design decisions based on that. Indexing is important in Mongo while there is non in Azure table that you can do.
Sharding consideration.
Redundancy Consideration.
In modern development/Architecture many principles has changed, each Microservice has its own database that might be totally different that any other Microservices'.
If you read along the guidelines that I provided, you will see what I mean.
Designing your Table service solution to be read efficient:
Design for querying in read-heavy applications. When you are designing your tables, think about the queries (especially the latency sensitive ones) that you will execute before you think about how you will update your entities. This typically results in an efficient and performant solution.
Specify both PartitionKey and RowKey in your queries. Point queries such as these are the most efficient table service queries.
Consider storing duplicate copies of entities. Table storage is cheap so consider storing the same entity multiple times (with different keys) to enable more efficient queries.
Consider denormalizing your data. Table storage is cheap so consider denormalizing your data. For example, store summary entities so that queries for aggregate data only need to access a single entity.
Use compound key values. The only keys you have are PartitionKey and RowKey. For example, use compound key values to enable alternate keyed access paths to entities.
Use query projection. You can reduce the amount of data that you transfer over the network by using queries that select just the fields you need.
Designing your Table service solution to be write efficient:
Do not create hot partitions. Choose keys that enable you to spread your requests across multiple partitions at any point of time.
Avoid spikes in traffic. Smooth the traffic over a reasonable period of time and avoid spikes in traffic.
Don't necessarily create a separate table for each type of entity. When you require atomic transactions across entity types, you can store these multiple entity types in the same partition in the same table.
Consider the maximum throughput you must achieve. You must be aware of the scalability targets for the Table service and ensure that your design will not cause you to exceed them.
Another good source is this link:
Background
We are looking at porting a 'monolithic' 3 tier Web app to a microservices architecture. The web app displays listings to a consumer (think Craiglist).
The backend consists of a REST API that calls into a SQL DB and returns JSON for a SPA app to build a UI (there's also a mobile app). Data is written to the SQL DB via background services (ftp + worker roles). There's also some pages that allow writes by the user.
Information required:
I'm trying to figure out how (if at all), Azure Service Fabric would be a good fit for a microservices architecture in my scenario. I know the pros/cons of microservices vs monolith, but i'm trying to figure out the application of various microservice programming models to our current architecture.
Questions
Is Azure Service Fabric a good fit for this? If not, other recommendations? Currently i'm leaning towards a bunch of OWIN-based .NET web sites, split up by area/service, each hosted on their own machine and tied together by an API gateway.
Which Service Fabric programming model would i go for? Stateless services with their own backing DB? I can't see how Stateful or Actor model would help here.
If i went with Stateful services/Actor, how would i go about updating data as part of a maintenance/ad-hoc admin request? Traditionally we would simply login to the DB and update the data, and the API would return the new data - but if it's persisted in-memory/across nodes in a cluster, how would we update it? Would i have to expose this all via methods on the service? Similarly, how would I import my existing SQL data into a stateful service?
For Stateful services/actor model, how can I 'see' the data visually, with an object Explorer/UI. Our data is our Gold, and I'm concerned of the lack of control/visibility of it in the reliable services models
Basically, is there some documentation on the decision path towards which programming model to go for? I could model a "listing" as an Actor, and have millions of those - sure, but i could also have a Stateful service that stores the listing locally, and i could also have a Stateless service that fetches it from the DB. How does one decide as to which is the best approach, for a given use case?
Thanks.
What is it about your current setup that isn't meeting your requirements? What do you hope to gain from a more complex architecture?
Microservices aren't a magic bullet. You mainly get four benefits:
You can scale and distribute pieces of your overall system independently. Service Fabric has very sophisticated tools and advanced capabilities for this.
You can deploy and upgrade pieces of your overall system independently. Service Fabric again has advanced capabilities for this.
You can have a polyglot system - each service can be written in a different language/platform.
You can use conflicting dependencies - each service can have its own set of dependencies, like different framework versions.
All of this comes at a cost and introduces complexity and new ways your system can fail. For example: your fast, compile-time checked in-proc method calls now become slow (by comparison to an in-proc function call) failure-prone network calls. And these are not specific to Service Fabric, btw, this is just what happens you go from in-proc method calls to cross-machine I/O - doesn't matter what platform you use. The decision path here is a pro/con list specific to your application and your requirements.
To answer your Service Fabric questions specifically:
Which programming model do you go for? Start with stateless services with ASP.NET Core. It's going to be the simplest translation of your current architecture that doesn't require mucking around with your data layer.
Stateful has a lot of great uses, but it's not necessarily a replacement for your RDBMS. A good place to start is hot data that can be stored in simple key-value pairs, is accessed frequently and needs to be low-latency (you get local reads!), and doesn't need to be datamined. Some examples include user session state, cache data, a "snapshot" of the most recent items in a data stream (like the most recent stock quote in a stream of stock quotes).
Currently the only way to see or query your data is programmatically directly against the Reliable Collection APIs. There is no viewer or "management studio" tool. You have to write (and secure) an API in each service that can display and query data.
Finally, the actor model is a very niche model. It serves specific purposes but if you just treat it as a data store it will not work for you. Like in your example, a listing per actor probably wouldn't work because you can't query across that list, or even have multiple users reading the same listing simultaneously.
I want to save logs and traces if my bulky , big enterprise app in DocumentDB.
so that those logs not only help developer to troubleshoot issues in production but also helps Business takes critical data driven decisions.
For such scenario does Mongo DB or Azure Doc DB suits ?
There is no right answer to this question - only opinions.
Here are some tradeoffs you may want to consider:
Pros:
Document-oriented databases, like DocumentDB, are schema-agnostic. This means the logging data's schema is dictated solely by the application. In other words, you can store log output without having to manage schema updates between both the application and database and keeping those models in sync (low friction).
DocumentDB automatically indexes every property in every document (record). This can speed up your ability to query off arbitrary attributes when debugging... which in turn, can reduce your time-to-mitigate when troubleshooting high-severity incidents.
Cons:
When compared to storing logs as blobs in a blob store... DocumentDB can look fairly expensive as a log store. You are paying a premium to able to easily index and quickly query off of the data you are storing. You will want to make sure you are getting value out of what you are paying for.
As the comments above suggested, NoSQL is an umbrella term that which encapsulates key-value store, column-oriented databases, document-oriented databases, graph databases, etc. I'd recommend taking a quick look at the differences between various database categories and understand the differences.
As with any project (logging or otherwise)... You should evaluate the tradeoffs you are making when picking between technologies. An important aspect to software engineering is making the right tradeoffs, and not checking feature tickboxes for the sake of checkboxes.
A brief summary of the project I'm working on:
I was hired as a web dev intern at a small company (part of a larger corporation) close to the state college I attend. For the past couple months, myself and two other interns have been working on the front-end as well as the back-end. The company is prototyping adding sensors to its products (oil/gas industry); we were tasked with building the portal that customers could login to to see data from their machines even if they're not near them.
Basically, we're collecting sensor data (~ten sensors/machine) and it's sent back to us. Where we're stuck is determining the best way to store and analyze long term data. We have a Redis Cache set up for fast access by the front-end, where only the lastest set of data for each machine is stored. But for historical data, I (and my coworkers) are having a tough time deciding the best route to go. Our whole project is based in VS (C#/Razor) with Azure integration (which is amazing by the way), so I'd like to keep the long term storage there as well. As far as I can tell, HDinsight + data in a BLOB seems to be the best option, but I'm fairly green when it comes to backend solutions. I would just like input from some older developers who may have more experience in this area, as we are the only developers here besides a couple older members who are more involved in the engineering side of things vs. development.
So, professionals of stack overflow, what would be your recommendation for long-term data storage and analytics?
PS: I apologize if I have HDinsight confused. From what I understand, it maps data in BLOB storage into HBase for easier analytics? Hadoop/HBase confuses me.
My first recommendation would be Azure Table storage. It provides a highly scalable and low cost data archival solution. If designed properly, you can also get a very decent query performance. Refer to the Azure Storage Table Design Guide for more details.
My second choice would be Azure DocumentDB service which is a NoSQL document database. It costs a bit more but querying data is much more flexible.
You should only go with HDInsight when you have a specific need as it's a resource-intensive and expensive service. Once you identify a specific requirement for a big-data analysis that's when you import your data and process it with HDInsight.
Im writing a 'proof of concept' application to investigate the possibility of moving a bespoke ASP.NET ecommerce system over to Windows Azure during a necessary re-write of the entire application.
Im tempted to look at using Azure Table Storage as an alternative to SQL Azure as the entities being stored are likely to change their schema (properties) over time as the application matures further, and I wont need to make endless database schema changes. In addition we can build refferential integrity into the applicaiton code - so the case for considering Azure Table Storage is a strong one.
The only potential issue I can see at this time is that we do a small amount of simple reporting - i.e. value of sales between two dates, number of items sold for a particular product etc.
I know that Table Storage doesnt support aggregate type functions, and I believe we can achieve what we want with clever use of partitions, multiple entity types to store subsets of the same data and possibly pre-aggregation but Im not 100% sure about how to go about it.
Does anyone know of any in-depth documents about Azure Table Storage design principles so that we make proper and efficient use of Tables, PartitionKeys and entity design etc.
there's a few simplistic documents around, and the current books available tend not to go into this subject in much depth.
FYI - the ecommerce site has about 25,000 customers and takes about 100,000 orders per year.
Have you seen this post ?
http://blogs.msdn.com/b/windowsazurestorage/archive/2010/11/06/how-to-get-most-out-of-windows-azure-tables.aspx
Pretty thorough coverage of tables
I think there are three potential issues I think in porting your app to Table Storage.
The lack of reporting - including aggregate functions - which you've already identified
The limited availability of transaction support - with 100,000 orders per year I think you'll end up missing this support.
Some problems with costs - $1 per million operations is only a small cost, but you can need to factor this in if you get a lot of page views.
Honestly, I think a hybrid approach - perhaps EF or NH to SQL Azure for critical data, with large objects stored in Table/Blob?
Enough of my opinion! For "in depth":
try the storage team's blog http://blogs.msdn.com/b/windowsazurestorage/ - I've found this very good
try the PDC sessions from Jai Haridas (couldn't spot a link - but I'm sure its there still)
try articles inside Eric's book - http://geekswithblogs.net/iupdateable/archive/2010/06/23/free-96-page-book---windows-azure-platform-articles-from.aspx
there's some very good best practice based advice on - http://azurescope.cloudapp.net/ - but this is somewhat performance orientated
If you have start looking at Azure storage such as table, it would do no harm in looking at other NOSQL offerings in the market (especially around document databases). This would give you insight into NOSQL space and how solution around such storages are designed.
You can also think about a hybrid approach of SQL DB + NOSQL solution. Parts of the system may lend themselves very well to Azure table storage model.
NOSQL solutions such as Azure table have their own challenges such as
Schema changes for data. Check here and here
Transactional support
ACID constraints. Check here
All table design papers I have seen are pretty much exclusively focused on the topics of scalability and search performance. I have not seen anything related to design considerations for reporting or BI.
Now, azure tables are accessible through rest APIs and via the azure SDK. Depending on what reporting you need, you might be able to pull out the information you require with minimal effort. If your reporting requirements are very sophisticated, then perhaps SQL azure together with Windows Azure SQL Reporting services might be a better option to consider?