I am trying to backup a database through sqlachemy and save it as a file. I tried using the extension, Flask-AlchemyDumps, but it appears to no longer be supported.
I musted be missing something obvious as this is surly an action a lot of developers want to do. Does anyone know how I should be backing up the database?
Thanks in advance
J Kirkman
SQLAlchemy is an ORM which sits between your code and the database. It's useful if you want to interact with specific rows and relationships without having to keep track of lots of ids and joins.
What you're looking for is a way to dump the entire contents of your DB to disk, presumably so you can restore it later/elsewhere. This is a bulk action, which is your first clue that an ORM may not be a suitable tool. (ORMs tend to be fast enough for small to medium operations, but slow and not ideal for actions which affect 10s of 1000s of rows at once.) And indeed, this isn't usually something you'd use an ORM for, it's a feature of your DB, presumably Postgres or MySQL. If you happen to be using Heroku, you can use their command line tool to do this.
Related
So we've recently setup sharding and we're migrating some of the data from several clients across several smaller databases into a bigger, sharded one. The problem is, that if I try to move data from production and do a mongorestore, then the files won't update if they have the same _id. This is a problem, because several mongorestores might be necessary as we test the sharded database and as customer production data changes over the testing period.
I obviously don't want to use --drop, since that will drop the whole collection instead of replacing the old files. Is there any way of doing this properly?
Cheers
I came up with a solution, although not ideal.
I'll use mongoimport with the --upsert option, instead of mongodump. For a whole database, I might need to write a script to mimic mongodump, but oh well.
Is it possible to "scratch" a couchdb document? By that I mean to delete a document, and make sure that the document and its history is completely removed from the database.
I do not want to perform a database compaction, I just want to fully wipe out a single document. And I am looking for a solution that guarantees that there is no trace of the document in the database, without needing to wait for internal database processes to eventually remove the document.
(a python solution is appreciated)
When you delete a document in CouchDB, generally only the _id, _rev, and a deleted flag are preserved. These are preserved to allow for eventual consistency through replication. Forcing an immediate delete across an entire group of nodes isn't really consistent with the architecture.
The closest thing would be to use purge; once you do that, all traces of the doc will be gone after the next compaction. I realize this isn't exactly what you're asking for, but it's the closest thing off the top of my head.
Here's a nice article explaining the basis behind the various delete methods available.
Deleting anything from file system for sure is difficult, and usually quite expensive problem. Even more with databases in general. Depending of what for sure means to you, you may end up with custom db, custom os and custom hw. So it is kind of saying I want fault tolerant system, yes everyone would like to have one, but only few can afford it, but good news is that most can settle for less. Similar is for deleteing for sure, I assume you are trying to adress some security or privacy issue, so try to see if there is some other way to get what you need. Perhaps encrypting the document or sensitive parts of it.
I'm in a VERY difficult problem.
I have a Microsoft Access Data Base, but it was made in the most chaotic way possible. The DB has like 150+ tables, Only uses like 50% of the tables. The relations are almost random. But, somehow, it delivers some information.
I need to get a particular component of the DB, but is so tangled that I can not manage to get into the table that creates that value. I revised every table, one by one, and found nothing.
I used mdbtools for Linux to try to inspect with more details the DB. But unfortunately has not been developed in years, and it closes every time. Maybe because the DB is "big" ? -700 mg-
I'm wondering: is there a way to see all the relations the arrives to the particular value I'm looking? Or to decompile the DB? I have no idea in which language it was made. I'm suspecting that it was made in Visual, just because is rather crappy.
Well, waiting for some help.
I would suggest using (still) MS Access for this. But, if relationships look messy on the diagram, you can query one of the system tables (MSysRelationships) directly to get ALL the relationships you need (e.g. for particular table etc.):
To unhide system tables in early versions of Access (97-2003), follow the instructions here:
For Access 2007, do the following:
I'm working on a "real-time" website using Nodejs. Currently, I'm using Redis because I need high performance for read-access. The write accesses are not really significant for my use case.
In addition, Redis does not have a query language for the search. So, I create my indexes manually and I use some unions/intersections/... to find some values.
I think that it will be easier to use MongoDB with a embedded finding system and a ORM-like (Mongoose for example). The problem is that I'm not sure that MongoDB is the best choice for my usecase.
What is your advices about the NoSQL DB that I need ? Redis ? CouchDB ? MongoDB ? Cassandra ? etc.
I repeat: I want to have a real good performance for the read accesses and for the searches (the write accesses are not significant), the simplest possible (orm-like ? finding system ? etc.)
Thanks.
I believe that redis would be the better solution for the following reasons.
You require fast read access and redis provides the fastest solution since the keys are in memory, if not most.
Although mongodb is easier to query in the general case, your problem domain is narrow and once you decide how you would like to query the data, you can put the correct data structures and indexes in place.
I would say that Redis is a good fit for your DB, and you should look at something like Solr or elasticsearch to provide your searching.
CouchDB will do better in write heavy environment. I don't use it though.
MongoDB will do better on read heavy environment.
For search and indexing:
MongoDB would require separate index for each of your search criteria for better performance (at least this is what I remember).
Proper index is important in MongoDB. And no joins!!
Here are some links you might go through:
http://www.mongodb.org/display/DOCS/Comparing+Mongo+DB+and+Couch+DB
http://www.snailinaturtleneck.com/blog/2009/06/29/couchdb-vs-mongodb-benchmark/
http://kkovacs.eu/cassandra-vs-mongodb-vs-couchdb-vs-redis
Hope these will help you find the right db
Goodluck
I am building a tool that searches people based on a number of attributes. The values for these attributes are scattered across several systems.
As an example, dateOfBirth is stored in a SQL Server database as part of system ABC. That person's sales region assignment is stored in some horrible legacy database. Other attributes are stored in a system only accessible over an XML web service.
To make matters worse, the the legacy database and the web service can be really slow.
What strategies and tips should I consider for implementing a search across all these systems?
Note: Although I posted an answer, I'm not confident its a great answer. I don't intend to accept my own answer unless no one else gives better insight.
You could consider using an indexing mechanism to retrieve and locally index the data across all the systems, and then perform your searches against the index. Searches would be an awful lot faster and more reliable.
Of course, this just shifts the problem from one part of your system to another - now your indexing mechanism has to handle failures and heterogeneous systems, but that may be an easier problem to solve.
Another factor is how often the data changes. If you have to query data in real-time that goes stale very quickly, then indexing may not be practical.
If you can get away with a restrictive search, start by returning a list based on the search criteria corresponding to the fastest data source. Then join up those records with the other systems and remove records which don't match the search criteria.
If you have to implement OR logic, this approach is not going to work.
While not an actual answer, this might at least get you partway to a workable solution. We had a similar situation at a previous employer - lots of data sources, different ways of accessing those data sources, different access permissions, military/government/civilian sources, etc. We used Mule, which is built around the Enterprise Service Bus concept, to connect these data sources to our application. My details are a bit sketchy, as I wasn't the actual implementor, just an integrator, but what we did was define a channel in Mule. Then you write a simple integration piece to go between the channel and the data source, and the application and the channel. The integration piece does the work of making the actual query, and formatting the results, so we had a generic SQL integration piece for accessing a database, and for things like web services, we had some base classes that implemented common functionality, so the actual customization of the integration piecess was a lot less work than it sounds like. The application could then query the channel, which would handle accessing the various data sources, transforming them into a normalized bit of XML, and return the results to the application.
This had a lot of advantages for our situation. We could include new data sources for existing queries by simply connecting them to the channel - the application didn't have to know or care what data sources where there, as it only looked at the data from the channel. Since data can be pushed or pulled from the channel, we could have a data source update the application when, for example, it was updated.
It took a while to get it configured and working, but once we got it going, we were pretty successful with it. In our demo setup, we ended up with 4 or 5 applications acting as both producers and consumers of data, and connecting to maybe 10 data sources.
Have you thought of moving the data into a separate structure?
For example, Lucene stores data to be searched in a schema-less inverted indexed. You could have a separate program that retrieves data from all your different sources and puts them in a Lucene index. Your search could work against this index and the search results could contain a unique identifier and the system it came from.
http://lucene.apache.org/java/docs/
(There are implementations in other languages as well)
Have you taken a look at YQL? It may not be the perfect solution but I might give you starting point to work from.
Well, for starters I'd parallelize the queries to the different systems. That way we can minimize the query time.
You might also want to think about caching and aggregating the search attributes for subsequent queries in order to speed things up.
You have the option of creating an aggregation service or middleware that aggregates all the different systems so that you can provide a single interface for querying. If you do that, this is where I'd do the previously mentioned cache and parallize optimizations.
However, with all of that it you will need weighing up the development time/deployment time /long term benefits of the effort against migrating the old legacy database to a faster more modern one. You haven't said how tied into other systems those databases are so it may not be a very viable option in the short term.
EDIT: in response to data going out of date. You can consider caching if your data if you don't need the data to always match the database in real time. Also, if some data doesn't change very often (e.g. dates of birth) then you should cache them. If you employ caching then you could make your system configurable as to what tables/columns to include or exclude from the cache and you could give each table/column a personalizable cache timeout with an overall default.
Use Pentaho/Kettle to copy all of the data fields that you can search on and display into a local MySQL database
http://www.pentaho.com/products/data_integration/
Create a batch script to run nightly and update your local copy. Maybe even every hour. Then, write your query against your local MySQL database and display the results.