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Problem:
Looking for best solution to store and make easily available big amount of weather data for the machine learning specialists team.
Initially I'm fetching data from cds.climate.copernicus.eu in netCDF or grib format. There will be some around 10-20Tb in grib or netCDF.
Requirements:
ML specialists can easily query data for given location (point, polygon) in given time range.
Results are returned in reasonable time.
Ideas:
Postgres. I thought that maybe pg would handle that amount of data. But the problem I encoutered with this is that loading data into postgres will take ages additionally it would take much more space than 10-20Tb (Because I planned to store that in row like format where you have two tables Point and WeatherMeasurement) Is it a good idea? Have anyone experience with this kind of data and pg?
Amazon Redshift. Would it be good approach to use this solution for weather data. How to load netCDf or grib into it? I have zero experience with could solutions like this.
Files. Just store data in the grib or netCDF files. I would write some simplified Python interface to fetch data from those files? But the questions is will the queries be fast enough? Have anyone experience with those?
For data this size that you want to sub-select quickly along multiple dimensions I'd lean toward Redshift. You will want to pay attention to how you want to query the data and establish the data model to provide the fastest access for the needed subsets. You may want to get some help setting this up initially as trial-and-error approach will take a while with this data size. Also Redshift isn't cheap at this scale so ask the budget questions too. This can be reduced if the database only needs to be up part of the time.
Files isn't a terrible idea as long as you can partition the data such that only a subset of files need to be accessed for any query. A partitioning strategy based on YEAR, MONTH, LAT-Decade, and LON-Decade might work - you'll need to understand what queries need to be performed and how fast (what's reasonable time?). This approach will be the least cost.
There is also a combo option - Redshift Spectrum. Redshift can utilize on database information AND in S3 stored data in the same queries. Again setting up the Redshift data model and S3 partitioning will be critical but this combo could give you attributes that will be valuable.
For any of these options you will want to convert to a more database friendly format like Parquet (or even CSV). This conversion process along with how to merge new data will need to be understood. There are lots of cloud tools to help with this processing.
Given the size of data you are working with I'll stress again that learning as you go will be time consuming. You will likely want to find experts in the tools you are working with (and at the data sizes you have) to get up quickly.
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I wonder what would be a more efficient way to partition Parquet data when storing it in S3.
In my cluster I currently have a folder data with a huge amount of Parquet files. I would like to change the way I save data in order to simplify the data retrieval.
I have two options. One option is to store Parquet files in the following folder path:
PARTITION_YEAR=2017/PARTITION_MONTH=07/PARTITION_DAY=12/my-parquet-files-go-here
or
PARTITION_DATE=20170712/my-parquet-files-go-here
Which of these two alternatives would be more recommended if I need to read a range of 7 days in Spark using spark.read.parquet?
Which alternative would be faster?
Since in both cases you are storing data with daily granularity, given the appropriate implementation at read time these two should be equivalent, but the former allows you to define better grained pruning based on your needs: you can easily get data for a whole year, a single month or a single day (or a combination of those) with well supported glob patterns.
I'd encourage you to use the former solution to be more flexible, as for your current use case the efficiency doesn't change significantly.
I would strongly advise against having many, many folders in your s3 store. Why? Spark uses S3 connectors which mimic directory trees through multiple HTTP requests: the deeper and wider the tree, the more inefficient this becomes, not least because AWS S3 throttles HTTP requests
The year/month/day naming scheme works well with hive & spark, but if you go into too much depth (by day, by hour) then you may experience worse performance than if you didn't.
The answer is quite simple... it depends on how you will query the data!
If you are querying purely on a range of days, then the second option is the easiest:
SELECT ...
FROM table
WHERE date BETWEEN ... AND ...
If you partition by month and day, you'd have to write a WHERE clause that uses both fields, which would be difficult if the desired 7-day range straddles two moths (eg 2018-05-27 to 2015-06-02):
SELECT ...
FROM table
WHERE (month = 5 and date BETWEEN 27 AND 31) OR
(month = 6 and date BETWEEN 1 AND 2)
This is the best way to make the partitions work, but is not very efficient for coding.
Thus, if you are using a WHERE on the date, then partition by date!
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I am developing a system which constains a lot of olap work. According to my research, column based data warehouse is the best choice. But I am puzzled to choose a good data warehouse product.
All the data warehouse comparison article I see is befor 2012,and there seems little article about it. Is data warehouse out-of-date? Hadoop HBase is better?
As far as I know, InfiniDB is a high performance open source data warehouse product, but it has not been maintained for 2 years https://github.com/infinidb/infinidb. And there is little document about InfiniDB . Has InfiniDB been abundanted by developers ?
Which is the best data warehouse product by now?
How do I incrementally move my Business data stored in the Mysql database to data warehouse ?
Thank you for your answer!
Data warehousing is still a hot topic, and HBase is not the fastes, but a very well known and compatible one (many applications build on it)
I have taken the Journey for a good Column store some years ago and finally went with InfiniDB because of the easy migration from plain mysql. its a nice piece of software, but it has still bugs, so i cannot fully recommend it to be used in production. (not without a 2nd failover instance).
However, MariaDB has picket up the InfiniDB technology and is porting it over to their MariaDB Database Server. This new product ist called MariaDB Columnstore[1], of with a testing build is available. They have already put a lot effort in it, so i think ColumnStore will get a Major product of MariaDB within the next two years.
I cant answer that. Im still with InfiniDB and also helping others with their projects.
This totally depends on your data structure and usage.
InfiniDB is great at querying, it had (in my tests) ~8% better performance than impala, however, while infinidb supports INSERT, UPDATE, DELETE and transactions it is not great on transactional workload. i.e. just moving a community driven website to infinidb where visitors always manipulating data will NOT work well. one insert with 10000 rows will work well, 10000 inserts with 1 row will kill it.
We deployed Infinidb for our customers to 'aid' the query performance of a regular mariadb installation - we created a tool that imports and updates MariaDB database tables into InfiniDB faster querying. manipulations on that table are still done in MairaDB and the changes get batch-imported into InfiniDB with 30 sec delay. as original and infinidb tables have the same structure and are accessable with api mysql, we just can switch the database connection and have super-fast SELECT queries. this works well for our use case.
We also built new statistics/analytics applications from ground up to work with infinidb and replace a older MySQL-Based System, which also works great and above any performance-expectations. (we now have 15x of the data we had in mariadb, and its still easier to maintain and much faster to query).
[1] https://mariadb.com/products/mariadb-columnstore
I would give Splice Machine a shot (Open Source). It stores data on HBase and will provide the core data management functions that a warehouse provides (Primary Keys, Constraints, Foreign Keys, etc.)
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I have a data-set that consist of 1500 columns and 6500 rows and I am trying to figure out what the best way is to shape the data for web based user interactive visualizations.
What I am trying to do is make the data more interactive and create an admin console that allows anyone to filter the data visually.
Front-end could potentially be based on Crossfilter, D3 and DC.js and give the user basically end-less filtering possibilities(date, value, country. In addition there will be some pre defined views like top and bottom 10 values.
I have seen and tested some great examples like this one, but after testing it did not really fit for the large amount of columns I had and it was based on a full JSON dump from the MongoDB. This amounted in very long loading times and loss of full interactivity with the data.
So in the end my question is what is the best approach (starting with normalization) in getting the data shaped in the right way so it can be manipulated from a front-end. Changing the amount of columns is a priority.
A quick look at the piece of data that you shared suggests that the dataset is highly denormalized. To allow for querying and visualization from a database backend I would suggest normalizing. This is no small bit software work but in the end you will have relational data which is much easier to deal with.
It's hard to guess where you would start but from the bit of data you showed there would be a country table, an event table of some sort and probably some tables of enumerated values.
In any case you will have a hard time finding a db engine that a lows that many columns. The row count is not a problem. I think in the end you will want a db with dozens of tables.
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I was thinking of using a database like mongodb or ravendb to store a lot of stock tick data and wanted to know if this would be viable compared to a standard relational such as Sql Server.
The data would not really be relational and would be a couple of huge tables. I was also thinking that I could sum/min/max rows of data by minute/hour/day/week/month etc for even faster calculations.
Example data:
500 symbols * 60 min * 60sec * 300 days... (per record we store: date, open, high,low,close, volume, openint - all decimal/float)
So what do you guys think?
Since when this question was asked in 2010, several database engines were released or have developed features that specifically handle time series such as stock tick data:
InfluxDB - see my other answer
Cassandra
With MongoDB or other document-oriented databases, if you target performance, the advices is to contort your schema to organize ticks in an object keyed by seconds (or an object of minutes, each minute being another object with 60 seconds). With a specialized time series database, you can query data simply with
SELECT open, close FROM market_data
WHERE symbol = 'AAPL' AND time > '2016-09-14' AND time < '2016-09-21'
I was also thinking that I could sum/min/max rows of data by minute/hour/day/week/month etc for even faster calculations.
With InfluxDB, this is very straightforward. Here's how to get the daily minimums and maximums:
SELECT MIN("close"), MAX("close") FROM "market_data" WHERE WHERE symbol = 'AAPL'
GROUP BY time(1d)
You can group by time intervals which can be in microseconds (u), seconds (s), minutes (m), hours (h), days (d) or weeks (w).
TL;DR
Time-series databases are better choices than document-oriented databases for storing and querying large amounts of stock tick data.
The answer here will depend on scope.
MongoDB is great way to get the data "in" and it's really fast at querying individual pieces. It's also nice as it is built to scale horizontally.
However, what you'll have to remember is that all of your significant "queries" are actually going to result from "batch job output".
As an example, Gilt Groupe has created a system called Hummingbird that they use for real-time analytics on their web site. Presentation here. They're basically dynamically rendering pages based on collected performance data in tight intervals (15 minutes).
In their case, they have a simple cycle: post data to mongo -> run map-reduce -> push data to webs for real-time optimization -> rinse / repeat.
This is honestly pretty close to what you probably want to do. However, there are some limitations here:
Map-reduce is new to many people. If you're familiar with SQL, you'll have to accept the learning curve of Map-reduce.
If you're pumping in lots of data, your map-reduces are going to be slower on those boxes. You'll probably want to look at slaving / replica pairs if response times are a big deal.
On the other hand, you'll run into different variants of these problems with SQL.
Of course there are some benefits here:
Horizontal scalability. If you have lots of boxes then you can shard them and get somewhat linear performance increases on Map/Reduce jobs (that's how they work). Building such a "cluster" with SQL databases is lot more costly and expensive.
Really fast speed and as with point #1, you get the ability to add RAM horizontally to keep up the speed.
As mentioned by others though, you're going to lose access to ETL and other common analysis tools. You'll definitely be on the hook to write a lot of your own analysis tools.
Here's my reservation with the idea - and I'm going to openly acknowledge that my working knowledge of document databases is weak. I’m assuming you want all of this data stored so that you can perform some aggregation or trend-based analysis on it.
If you use a document based db to act as your source, the loading and manipulation of each row of data (CRUD operations) is very simple. Very efficient, very straight forward, basically lovely.
What sucks is that there are very few, if any, options to extract this data and cram it into a structure more suitable for statistical analysis e.g. columnar database or cube. If you load it into a basic relational database, there are a host of tools, both commercial and open source such as pentaho that will accommodate the ETL and analysis very nicely.
Ultimately though, what you want to keep in mind is that every financial firm in the world has a stock analysis/ auto-trader application; they just caused a major U.S. stock market tumble and they are not toys. :)
A simple datastore such as a key-value or document database is also beneficial in cases where performing analytics reasonably exceeds a single system's capacity. (Or it will require an exceptionally large machine to handle the load.) In these cases, it makes sense to use a simple store since the analytics require batch processing anyway. I would personally look at finding a horizontally scaling processing method to coming up with the unit/time analytics required.
I would investigate using something built on Hadoop for parallel processing. Either use the framework natively in Java/C++ or some higher level abstraction: Pig, Wukong, binary executables through the streaming interface, etc. Amazon offers reasonably cheap processing time and storage if that route is of interest. (I have no personal experience but many do and depend on it for their businesses.)
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I'm comfortable in the MySQL space having designed several apps over the past few years, and then continuously refining performance and scalability aspects. I also have some experience working with memcached to provide application side speed-ups on frequently queried result sets. And recently I implemented the Amazon SDB as my primary "database" for an ecommerce experiment.
To oversimplify, a quick justification I went through in my mind for using the SDB service was that using a schema-less database structure would allow me to focus on the logical problem of my project and rapidly accumulate content in my data-store. That is, don't worry about setting up and normalize all possible permutations of a product's attributes before hand; simply start loading in the products and the SDB will simply remember everything that is available.
Now that I have managed to get through the first few iterations of my project and I need to setup simple interfaces to the data, I am running to issues that I had taken for granted working with MySQL. Ex: grouping in select statements and limit syntax to query "items 50 to 100". The ease advantage I gained using schema free architecture of SDB, I lost to a performance hit of querying/looping a resultset with just over 1800 items.
Now I'm reading about projects like Tokyo Cabinet that are extending the concept of in-memory key-value stores to provide pseudo-relational functionality at ridiculously faster speeds (14x i read somewhere).
My question:
Are there some rudimentary guidelines or heuristics that I as an application designer/developer can go through to evaluate which DB tech is the most appropriate at each stage of my project.
Ex: At a prototyping stage where logical/technical unknowns of the application make data structure fluid: use SDB.
At a more mature stage where user deliverables are a priority, use traditional tools where you don't have to spend dev time writing sorting, grouping or pagination logic.
Practical experience with these tools would be very much appreciated.
Thanks SO!
Shaheeb R.
The problems you are finding are why RDBMS specialists view some of the alternative systems with a jaundiced eye. Yes, the alternative systems handle certain specific requirements extremely fast, but as soon as you want to do something else with the same data, the fleetest suddenly becomes the laggard. By contrast, an RDBMS typically manages the variations with greater aplomb; it may not be quite as fast as the fleetest for the specialized workload which the fleetest is micro-optimized to handle, but it seldom deteriorates as fast when called upon to deal with other queries.
The new solutions are not silver bullets.
Compared to traditional RDBMS, these systems make improvements in some aspect (scalability, availability or simplicity) by trading-off other aspects (reduced query capability, eventual consistency, horrible performance for certain operations).
Think of these not as replacements of the traditional database, but they are specialized tools for a known, specific need.
Take Amazon Simple DB for example, SDB is basically a huge spreadsheet, if that is what your data looks like, then it probably works well and the superb scalability and simplicity will save you a lot of time and money.
If your system requires very structured and complex queries but you insist with one of these cool new solution, you will soon find yourself in the middle of re-implementing a amateurish, ill-designed RDBMS, with all of its inherent problems.
In this respect, if you do not know whether these will suit your need, I think it is actually better to do your first few iterations in a traditional RDBMS because they give you the best flexibility and capability especially in a single server deployment and under modest load. (see CAP Theorem).
Once you have a better idea about what your data will look like and how will they be used, then you can match your need with an alternative solution.
If you want the simplicity of a cloud hosted solution, but needs a relational database, you can check out: Amazon Relational Database Service