I have a collection of text files containing anonymised medical data (age, country, symptoms, diagnosis etc). This data goes back for at least 30 years so as you can imagine I have quite a large sized data set. In total I have around 20,000 text files totalling approx. 1TB.
Periodically I will be needing to search these files for occurances of a particular string (not regex). What is the quickest way to search through this data?
I have tried using grep and recursively searching through the directory as follows:
LC_ALL=C fgrep -r -i "searchTerm" /Folder/Containing/Files
The only problem with doing the above is that it takes hours (sometimes half a day!) to search through this data.
Is there a quicker way to search through this data? At this moment I am open to different approaches such as databases, elasticsearch etc. If I do go down the database route, I will have approx. 1 billion records.
My only requirements are:
1) The search will be happening on my local computer (Dual-Core CPU and 8GB RAM)
2) I will be searching for strings (not regex).
3) I will need to see all occurances of the search string and the file it was within.
There are a lot of answers already, I just wanted to add my two cents:
Having this much huge data(1 TB) with just 8 GB of memory will not be good enough for any approach, be it using the Lucene or Elasticsearch(internally uses Lucene) or some grep command if you want faster search, the reason being very simple all these systems hold the data in fastest memory to be able to serve faster and out of 8 GB(25% you should reserve for OS and another 25-50% at least for other application), you are left with very few GB of RAM.
Upgrading the SSD, increasing RAM on your system will help but it's quite cumbersome and again if you hit performance issues it will be difficult to do vertical scaling of your system.
Suggestion
I know you already mentioned that you want to do this on your system but as I said it wouldn't give any real benefit and you might end up wasting so much time(infra and code-wise(so many approaches as mentioned in various answers)), hence would suggest you do the top-down approach as mentioned in my another answer for determining the right capacity. It would help you to identify the correct capacity quickly of whatever approach you choose.
About the implementation wise, I would suggest doing it with Elasticsearch(ES), as it's very easy to set up and scale, you can even use the AWS Elasticsearch which is available in free-tier as well and later on quickly scale, although I am not a big fan of AWS ES, its saves a lot of time of setting up and you can quickly get started if you are much familiar of ES.
In order to make search faster, you can split the file into multiple fields(title,body,tags,author etc) and index only the important field, which would reduce the inverted index size and if you are looking only for exact string match(no partial or full-text search), then you can simply use the keyword field which is even faster to index and search.
I can go on about why Elasticsearch is good and how to optimize it, but that's not the crux and Bottomline is that any search will need a significant amount of memory, CPU, and disk and any one of becoming bottleneck would hamper your local system search and other application, hence advising you to really consider doing this on external system and Elasticsearch really stands out as its mean for distributed system and most popular open-source search system today.
You clearly need an index, as almost every answer has suggested. You could totally improve your hardware but since you have said that it is fixed, I won’t elaborate on that.
I have a few relevant pointers for you:
Index only the fields in which you want to find the search term rather than indexing the entire dataset;
Create multilevel index (i.e. index over index) so that your index searches are quicker. This will be especially relevant if your index grows to more than 8 GB;
I wanted to recommend caching of your searches as an alternative, but this will cause a new search to again take half a day. So preprocessing your data to build an index is clearly better than processing the data as the query comes.
Minor Update:
A lot of answers here are suggesting you to put the data in Cloud. I'd highly recommend, even for anonymized medical data, that you confirm with the source (unless you scraped the data from the web) that it is ok to do.
To speed up your searches you need an inverted index. To be able to add new documents without the need to re-index all existing files the index should be incremental.
One of the first open source projects that introduced incremental indexing is Apache Lucense. It is still the most widely used indexing and search engine although other tools that extend its functionality are more popular nowadays. Elasiticsearch and Solr are both based on Lucense. But as long as you don't need a web frontend, support for analytical querying, filtering, grouping, support for indexing non-text files or an infrastrucutre for a cluster setup over multiple hosts, Lucene is still the best choice.
Apache Lucense is a Java library, but it ships with a fully-functional, commandline-based demo application. This basic demo should already provide all the functionality that you need.
With some Java knowledge it would also be easy to adapt the application to your needs. You will be suprised how simple the source code of the demo application is. If Java shouldn't be the language of your choice, its wrapper for Pyhton, PyLucene may also be an alternative. The indexing of the demo application is already reduced nearly to the minimum. By default no advanced functionlity is used like stemming or optimization for complex queries - features, you most likely will not need for your use-case but which would increase size of the index and indexing time.
I see 3 options for you.
You should really consider upgrading your hardware, hdd -> ssd upgrade can multiply the speed of search by times.
Increase the speed of your search on the spot.
You can refer to this question for various recommendations. The main idea of this method is optimize CPU load, but you will be limited by your HDD speed. The maximum speed multiplier is the number of your cores.
You can index your dataset.
Because you're working with texts, you would need some full text search databases. Elasticsearch and Postgres are good options.
This method requires you more disk space (but usually less than x2 space, depending on the data structure and the list of fields you want to index).
This method will be infinitely faster (seconds).
If you decide to use this method, select the analyzer configuration carefully to match what considered to be a single word for your task (here is an example for Elasticsearch)
Worth covering the topic from at two level: approach, and specific software to use.
Approach:
Based on the way you describe the data, it looks that pre-indexing will provide significant help. Pre-indexing will perform one time scan of the data, and will build a a compact index that make it possible to perform quick searches and identify where specific terms showed in the repository.
Depending on the queries, it the index will reduce or completely eliminate having to search through the actual document, even for complex queries like 'find all documents where AAA and BBB appears together).
Specific Tool
The hardware that you describe is relatively basic. Running complex searches will benefit from large memory/multi-core hardware. There are excellent solutions out there - elastic search, solr and similar tools can do magic, given strong hardware to support them.
I believe you want to look into two options, depending on your skills, and the data (it will help sample of the data can be shared) by OP.
* Build you own index, using light-weight database (sqlite, postgresql), OR
* Use light-weight search engine.
For the second approach, using describe hardware, I would recommended looking into 'glimpse' (and the supporting agrep utility). Glimple provide a way to pre-index the data, which make searches extremely fast. I've used it on big data repository (few GB, but never TB).
See: https://github.com/gvelez17/glimpse
Clearly, not as modern and feature rich as Elastic Search, but much easier to setup. It is server-less. The main benefit for the use case described by OP is the ability to scan existing files, without having to load the documents into extra search engine repository.
Can you think about ingesting all this data to elasticsearch if they have a consistent data structure format ?
If yes, below are the quick steps:
1. Install filebeat on your local computer
2. Install elasticsearch and kibana as well.
3. Export the data by making filebeat send all the data to elasticsearch.
4. Start searching it easily from Kibana.
Fs Crawler might help you in indexing the data into elasticsearch.After that normal elasticsearch queries can you be search engine.
I think if you cache the most recent searched medical data it might help performance wise instead of going through the whole 1TB you can use redis/memcached
I currently have a REST endpoint with basic CRUD operations for a sqlite database.
But my application updates whole tables at a time (with a "save" button)
My current idea/solution is to query the data first, compare the data, and update only the "rows" that changed.
The solution is a bit complex because there are several different types of changes that can be done:
Add row
Remove row
Row content changed (similar to content moving up or down)
Is there a simpler solution?
The most simplest solution is a bit dirty. (Delete table, create table and add each row back)
The solution is a bit complex because there are several different types of changes that can be done:
Add row
Remove row
Row content changed (similar to content moving up or down)
Is there a simpler solution?
The simple answer is
Yes, you are correct.
That is exactly how you do it.
There is literally no easy way to do this.
Be aware that, for example, Firebase entirely exists to do this.
Firebase is worth billions, is far from perfect, and was created by the smartest minds around. It literally exists to do exactly what you ask.
Again there is literally no easy solution to what you ask!
Some general reading:
One of the handful of decent articles on this:
https://www.objc.io/issues/10-syncing-data/data-synchronization/
Secondly you will want to familiarize yourself with Firebase, since, a normal part of computing now is either using baas sync solutions (eg Firebase, usually some noSql solution), or indeed doing it by hand.
http://firebase.google.com/docs/ios/setup/
(I don't especially recommend Firebase, but you have to know how to use it in as much as you have to know how to do regex and you have to know how to write sql calls.)
Finally you can't make realistic iOS apps without Core Data,
https://developer.apple.com/library/archive/documentation/Cocoa/Conceptual/CoreData/index.html
By no means does core data solve the problem you describe, but, realistically you will use it while you solve the problem conceptually.
You may enjoy realm,
https://realm.io
which again is - precisely - a solution to the problem you describe. (Which is basically the basic problem in typical modern app development.) As with FBase, even if you don't like it or decide not to go with it on a particular project, one needs to be familiar with it.
Azure's SQL database feature for auto-tuning creates and drops indexes based on database usage. I've imported an old database into Azure which did not have comprehensive indexes defined and it seems to of done a great job on reducing CPU & DTU usage over a relatively short period of time.
It feels wrong - but does this mean I can develop going forwards without defining indexes? Does anyone do this? SSMS index editor is a pain and slow to modify with. Not having to worry/maintain indexes would speed up development time.
The auto-tuning is taking advantage of three things, Query Store, Missing Indexes and Machine Learning.
First, the last known good plan is a weaponization of the Query Store. This is in both Azure and SQL Server 2017. It will spot queries that have degraded performance after a plan change (and quite a few executions, not just one) and will revert back to that plan. If performance degrades, it turns that off. This is great. However, if you write crap code or have bad data structures or out of date statistics, it doesn't help very much.
The automatic indexes in Azure are using two things, missing index suggestions from the optimizer and machine learning on Azure. With those, if the missing index comes up a lot over a period of 12-18 hours (read this blog post on automating it), you'll get an index suggestion. It measures for another 12-18 hours and if that index helped, it stays, if not, it goes. This is also great. However, it suffers from two problems. First, same as before, if you have crap code, etc., this will only really help at the margins. Second, the missing index suggestions from the optimizer are not always the best index. For example, when I wrote the blog post, it identified a missing index appropriately, but it missed the fact that an INCLUDE column would have been even better than the index it suggested.
A human brain and eyeball is still going to be solving the more difficult problems. These automations take away a lot of the easier, low-hanging problems. Overall, that's a great thing. However, don't confuse it with a panacea for all things performance related. It's absolutely not.
Does anyone know if there's a recommended set of database indexes for Orchard's core modules? I can't seem to find much info around this, and while I appreciate that the code uses NHibernate to abstract the underlying database, I suspect 99% of users will simply be using SQL server/Express as the default DB and would require suitable indexes to be added. For example on: Orchard_Framework_ContentItemVersionRecord ([Published], [ContentItemRecord_id])
If there isn't already, would it be a good idea for the core modules to have a recommended set of indexes documented somewhere, as they're clearly going to be required for any serious deployments based on an RDBMS?
You're right that some indices would help queries. However with indices it really depends on the usage pattern so there are not many built-in; but you can always add them yourself depending on your specific usage (you can also use SQL Server's tools to recommend indices for your). You can even add indices from migrations to other modules' tables.
In the latest source of Orchard (not yet released) there are also some more default indicies, also for ContentItemVersionRecord. You can see them in FrameworkDataMigration in the 1.x branch.
Is Lucene.Net suitable as the search engine for frequently changing content?
Or more specificically, can anybody give a subjective opinion on how quickly lucene.net indexes can be updated. Any other approaches to searching frequently changing content would be great.
We’re developing a forum. Forum posts will be frequently added to the forum repository. We think we need these posts to be added to lucene index very quickly (<0.5s) to become available to search. There’ll be about 5E6 posts in the repository initially. Assume search engine running on non-exotic server (I know this is very vague!).
Other suggestions with regard to addressing the issue of searching frequently changing content appreciated. The forum posts need to be searchable on a variable number of named tags (tag name and value must match). A SQL based approach (based on Toxi schema) isn’t giving us the performance we’d like.
Out forums (http://episteme.arstechnica.com) use Lucene as the search backend, so it's doable. Posts aren't indexed quite as quickly as you'd like, but we could solve that by beefing up the indexing hardware and using a smarter caching strategy.
The general answer to this question is: it depends what your write/update pattern is. Forums are relatively easy, since most content is new and existing content is updated less frequently.
For a forum, I'd recommend having an "archive" index and a "live" index. The live index might include posts from the last day, week, year, while the archive index will include a large body of posts that probably won't ever be touched again. So when someone creates a new post, it will initially be indexed in the live index. At a later time, some batch job would clear out the live index, and reindex everything into the archive.
Lucene's very good at querying across multiple indexes. You should abuse that ability. :)
Lucene.Net is extremely fast, however there are many things that can slow down queries when used wrong. I strongly recommend reading the Lucene in Action book by Erik Hatcher and Otis Gospodnetić. It contains a very good chapter about performance testing and tuning.