Has anyone had any experience with database partitioning? We already have a lot of data and queries on it are already starting to slow down. Maybe someone has some examples? These are tables related to orders.
Shopware, since version 6.4.12.0, allows the use of database clusters, see the relevant documentation. You will have to set up a number read-only nodes first. The load of reading data will then be distributed among the read-only nodes while write operations are restricted to the primary node.
Note that in a cluster setup you should also use a lock storage that compliments the setup.
Besides using a DB cluster you can also try to reduce the load of the db server.
The first thing you should enable the HTTP-Cache, still better to additionaly also set up a reverse cache like varnish. This will greatly decrease the number of requests that hit your webserver and thus your DB server as well.
Besides all those measures explained here should improve the overall performance of your shop as well as decreasing load on the DB.
Additionally you could use Elasticsearch, so that costly search requests won't hit the Database. And use a "real" MessageQueue, so that the messages are not stored in the Database. And use Redis instead of the database for the storage of performance critical information as is documented in the articles in this category of the official docs.
The impact of all those measures probably depends on your concrete project setup, so maybe you see in the DB locks something that hints to one of the points i mentioned previously, so that would be an indicator to start in that direction. E.g. if you see a lot of search related queries Elasticsearch would be a great start, but if you see a lot of DB load coming from writing/reading/deleting messages, then the MessageQueue might be a better starting point.
All in all when you use a DB cluster with a primary and multiple replicas and use the additional services i mentioned here your shop should be able to scale quite well without the need for partitioning the actual DB.
Related
we want to develop a dashboard to analyze geospatial data.
This is a small and close approach to what we want to do: http://adilmoujahid.com/images/data-viz-talkingdata.gif
Our main concerns are about the backend technologies to be used. (front will be D3.js, DC.js, leaflet.js...)
Between Django and node.js, we think that we will use node.js, cause we've read than its faster than Django for this kind of tasks. But we are not sure and we are open to ideas.
But about Mongo or Cassandra, we are so confused. Our data is mostly structured, so store it in tables like Cassandra would make it easy to manage, also Cassandra seems to have better performance. However, we also have IoT devices data, with lots of real-time GPS location...
Which suggestions can you give to us to achieve our goal?
TL;DR Summary;
Dashboard with hundreds of simultaneous users.
Stored data will be mostly structured text/numbers, but will include also images, GPS-arrays, IoT sensors, geographical data (vector-polygons & rasters)
Databases will receive high write load coming from sensors.
Dashboard performance is so important. Its more important to read data in real time, than keeping it uncorrupted/secure.
Most calculus/math will be calculated in the client's browser, the server will try to avoid mathematical operations.
Disclaimer: I'm a DataStax employee so I'll comment on the Cassandra piece.
Cassandra is a good choice for this if your dashboard can be planned around a set of known queries. If those users will be doing ad-hoc queries directly to the database from the dashboard, you'll want something with a little more flexibility like ElasticSearch or (shameless plug) DataStax Search. Especially if you expect the queries/database to handle some of the geospatial logic.
JaguarDB has very strong support of geospatial data (2D and 3D). It allows you to store multi-measurements per point location while other databases support only one measurement (pointm). Many complex queries such as Voronoi polygon, convexhull are also supported. It is open source, distributed and sharded, multiple columns indexes, etc.
Concerning Postgresql and Cassandra, is there much difference in RAM/CPU/DISK usage between them?
Our use case does not require transactions, it will be in a single node and we will have IoT devices writing data up to 500 times per second. However ive read that Geographical data that works better with Potstgis than cassandra...
According to this use case, do you recommend Cassandra or Postgis?
We plan to use Cassandra 3.x and we want to allow our customers to connect to Cassandra directly for exporting the data into their data warehouses.
They will connect via ODBC from remote.
Is there any way to prevent that the customer executes huge or bad SELECT statements that will result in a high load for all nodes? We use an extra data center in our replication strategy where only customers can connect, so live system will not be affected. But we want to setup some workers that will run on this shadow system also. Most important thing is, that a connected remote client will not have any noticable impact on other remote connections or our local worker jobs. There is a materialized view already and I want to force customers to get data based on primary key only (i.e. disallow usage of ALLOW FILTERING). It would be great also, if one can limit the number of rows returned (e.g. 1 million) to prevent a pull of all data.
Is there a best practise for this use case?
I know of BlackRocks video related to multi-tenant strategy in C* which advises to use tenant_id in schema. That is what we're doing already, but how can I ensure security/isolation via ODBC connected tenants/customers? Or do I have to write an API on my own which handles security?
I would recommend to expose access via API, not via ODBC - at least you would have greater control on what is executed, and enforce tenant_id, and other checks, like limits, etc. You can try to utilize the Cassandra's CQL parser to decompose query, and put all required things back.
Theoretically, you can could utilize Apache Calcite, for example. It has implementation of JDBC driver that could be used, plus there is existing Cassandra adapter that you can modify to accomplish your task (mapping authentication into tenant_ids, etc.), but this will be quite a lot of work.
I've got an application that's outgrowing SQL Azure - at the price I'm willing to pay, at any rate - and I'm interested in investigating Azure DocumentDB. The preview clearly has distinct scalability limits (as described here, for instance), but I think I could probably get away with those for the preview period, provided I'm using it correctly.
So here's the question I've got. How do I need to design my application to take advantage of the built-in scalability of the Azure DocumentDB? For instance, I know that with Azure Table Storage - that cheap but awful highly limited alternative - you need to structure all your data in a two-step hierarchy: PartitionKey and RowKey. Provided you do that (which is nigh well impossible in a real-world application), ATS (as I understand it) moves partitions around behind the scenes, from machine to machine, so that you get near-infinite scalability. Awesome, and you never have to think about it.
Scaling out with SQL Server is obviously much more complicated - you need to design your own sharding system, deal with figuring out which server the shard in question sits on, and so forth. Possible, and done right quite scalable, but complex and painful.
So how does scalability work with DocumentDB? It promises arbitrary scalability, but how does the storage engine work behind the scenes? I see that it has "Databases", and each database can have some number of "Collections", and so forth. But how does its arbitrary scalability map to these other concepts? If I have a SQL table that contains hundreds of millions of rows, am I going to get the scalability I need if I put all this data into one collection? Or do I need to manually spread it across multiple collections, sharded somehow? Or across multiple DB's? Or is DocumentDB somehow smart enough to coalesce queries in a performant way from across multiple machines, without me having to think about any of it? Or...?
I've been looking around, and haven't yet found any guidance on how to approach this. Very interested in what other people have found or what MS recommends.
Update: As of April 2016, DocumentDB has introduced the concept of a partitioned collection which allows you scale-out and take advantage of server-side partitioning.
A single DocumentDB database can scale practically to an unlimited amount of document storage partitioned by collections (in other words, you can scale out by adding more collections).
Each collection provides 10 GB of storage, and an variable amount of throughput (based on performance level). A collection also provides the scope for document storage and query execution; and is also the transaction domain for all the documents contained within it.
Source: http://azure.microsoft.com/en-us/documentation/articles/documentdb-manage/
Here's a link to a blog post I wrote on scaling and partitioning data for a multi-tenant application on DocumentDB.
With the latest version of DocumentDB, things have changed. There is still the 10GB limit per collection but in the past, it was up to you to figure out how to split up your data into multiple collections to avoid hitting the 10 GB limit.
Instead, you can now, specify a partition key and DocumentDB now handles the partitioning for you e.g. If you have log data, you may want to partition the data on the date value in your JSON document, so that each day a new partition is created.
You can fan out queries like this - http://stuartmcleantech.blogspot.co.uk/2016/03/scalable-querying-multiple-azure.html
I need to build a analytics server for large scale (seven figures and up) quickly and for the cheap.
Piwik would be the easy choice but for what I've gathered so far, Piwik is rather hard to scale and can require rather hefty servers to handle loads.
My second idea would to create quick and dirty Node.js server which just pushes everything to Amazon DynamoDB, so that one can start gathering the data from the day one and then build the UI later on. That would be quick to create and scale (vertically and horizontally). However, I'm wondering if DynamoDB is the right choice for such use? (gather data, generate reports)
I'm using DynamoDB professionaly and would not use it for your application.
DynamoDB truly has tons of constraints. Among them, you can have only one hash_key and optionally, one range_key.
You may do some "analytics" for items grouped under a given hash_key using query but really nothing fancy. For complex queries, you would have to use scan or EMR which are slow and expensive and have a couple of drawbacks due to throttling.
Nonetheless, NoSQL seems a good choice, at least for the prototyping stage of your application. But, I would recommend MongoDB instead. You can index any column, do complex queries, do not worry about data throttling. Sharding and replications is not too hard to setup.
MongoDB has a strong ecosystem and community which DynamoDB has not (yet) as it is much younger. MongoDB also has hosted offers which would allow you to bootstrap your application as quickly as you would with DynamoDB.
Piwik scales up to millions of pages & dozens of thousands of tracked websites per month. See their docs: http://piwik.org/docs/optimize/ and: http://piwik.org/blog/2012/07/piwik-high-scale-performance-report-as-of-july-2012/
The Setup:
Imagine a 'twitter like' service where a user submits a post, which is then read by many (hundreds, thousands, or more) users.
My question is regarding the best way to architect the cache & database to optimize for quick access & many reads, but still keep the historical data so that users may (if they want) see older posts. The assumption here is that 90% of users would only be interested in the new stuff, and that the old stuff will get accessed occasionally. The other assumption here is that we want to optimize for the 90%, and its ok if the older 10% take a little longer to retrieve.
With this in mind, my research seems to strongly point in the direction of using a cache for the 90%, and then to also store the posts in another longer-term persistent system. So my idea thus far is to use Redis for the cache. The advantages is that Redis is very fast, and also it has built in pub/sub which would be perfect for publishing posts to many people. And then I was considering using MongoDB as a more permanent data store to store the same posts which will be accessed as they expire off of Redis.
Questions:
1. Does this architecture hold water? Is there a better way to do this?
2. Regarding the mechanism for storing posts in both the Redis & MongoDB, I was thinking about having the app do 2 writes: 1st - write to Redis, it then is immediately available for the subscribers. 2nd - after successfully storing to Redis, write to MongoDB immediately. Is this the best way to do it? Should I instead have Redis push the expired posts to MongoDB itself? I thought about this, but I couldn't find much information on pushing to MongoDB from Redis directly.
It is actually sensible to associate Redis and MongoDB: they are good team players. You will find more information here:
MongoDB with redis
One critical point is the resiliency level you need. Both Redis and MongoDB can be configured to achieve an acceptable level of resiliency, and these considerations should be discussed at design time. Also, it may put constraint on the deployment options: if you want master/slave replication for both Redis and MongoDB you need at least 4 boxes (Redis and MongoDB should not be deployed on the same machine).
Now, it may be a bit simpler to keep Redis for queuing, pub/sub, etc ... and store the user data in MongoDB only. Rationale is you do not have to design similar data access paths (the difficult part of this job) for two stores featuring different paradigms. Also, MongoDB has built-in horizontal scalability (replica sets, auto-sharding, etc ...) while Redis has only do-it-yourself scalability.
Regarding the second question, writing to both stores would be the easiest way to do it. There is no built-in feature to replicate Redis activity to MongoDB. Designing a daemon listening to a Redis queue (where activity would be posted) and writing to MongoDB is not that hard though.