I am trying to figure out exactly what these new fangled data stores such as bigtable, hbase and cassandra really are.
I work with massive amounts of stock market data, billions of rows of price/quote data that can add up to 100s of gigabytes every day (although these text files often compress by at least an order of magnitude). This data is basically a handful of numbers, two or three short strings and a timestamp (usually millisecond level). If I had to pick a unique identifier for each row, I would have to pick the whole row (since an exchange may generate multiple values for the same symbol in the same millisecond).
I suppose the simplest way to map this data to bigtable (I'm including its derivatives) is by symbol name and date (which may return a very large time series, more than million data points isn't unheard of). From reading their descriptions, it looks like multiple keys can be used with these systems. I'm also assuming that decimal numbers are not good candidates for keys.
Some of these systems (Cassandra, for example) claims to be able to do range queries. Would I be able to efficiently query, say, all values for MSFT, for a given day, between 11:00 am and 1:30 pm ?
What if I want to search across ALL symbols for a given day, and request all symbols that have a price between $10 and $10.25 (so I'm searching the values, and want keys returned as a result)?
What if I want to get two times series, subtract one from the other, and return the two times series and their result, will I have to do his logic in my own program?
Reading relevant papers seems to show that these systems are not a very good fit for massive time series systems. However, if systems such as google maps are based on them, I think time series should work as well. For example, think of time as the x-axis, prices as y-axis and symbols as named locations--all of a sudden it looks like bigtable should be the ideal store for time series (if the whole earth can be stored, retrieved, zoomed and annotated, stock market data should be trivial).
Can some expert point me in the right direction or clear up any misunderstandings.
Thanks
I am not an expert yet, but I've been playing with Cassandra for a few days now, and I have some answers for you:
Don't worry about amount of data, it's irrelevant with systems like Cassandra, if you have $$$ for a large hardware cluster.
Some of these systems (Cassandra, for example) claims to be able to do range queries. Would I be able to efficiently query, say, all values for MSFT, for a given day, between 11:00 am and 1:30 pm ?
Cassandra is very useful when you know how to work with keys. It can swift through keys very quickly. So to search for MSFT between 11:00 and 1:30pm, you'd have to key your rows like this:
MSFT-timestamp, GOOG-timestamp , ..etc
Then you can tell Cassandra to find all keys that start with MSFT-now and end with MSFT-now+1hour.
What if I want to search across ALL symbols for a given day, and request all symbols that have a price between $10 and $10.25 (so I'm searching the values, and want keys returned as a result)?
I am not an expert, but so far I realized that Cassandra doesn't' search by values at all. So if you want to do the above, you will have to make another table dedicated just to this problem and design your schema to fit the case. But it won't be much different from what I described above. It's all about naming your keys and columns. Cassandra can find them very quickly!
What if I want to get two times series, subtract one from the other, and return the two times series and their result, will I have to do his logic in my own program?
Correct, all logic is done inside your program. This is not MySQL. This is just a storage engine. (But I am sure the next versions will offer these sort of things)
Please remember, that I am a novice at this, if I am wrong, feel free to correct me.
If you're dealing with a massive time series database, then the standards are:
KDB: http://www.kx.com/
OneTick: http://www.onetick.com
Vhayu: http://www.vhayu.com
These aren't cheap, but they can handle your data very efficiently.
Someone whom I respect recommended the Open Time Series Database. In particular, that the schema was the nicest he had ever seen.
http://opentsdb.net/
'Am standing in front of the same mountain. My main problem with cassandra is that I cannot get a stream on the result set, for example in the form of an iterator.
I am looking already up and down the docs and the net, but nothing.
I can't fetch all the keys and then get the rows as billions of rows makes this impossible.
The DataStax Java Driver allows for automatic paging so that will stream the results just like an iterator and it's all built in. This is in Cassandra 2.0.1 by the way - http://www.datastax.com/dev/blog/client-side-improvements-in-cassandra-2-0
Just for the sake of completeness reading this in 2018, there is now a special database just for timeseries data called TimescaleDB
http://www.timescale.com/
This blog is worth reading, it explains why it´s superior to solutions like Cassandra for that special case and why they decided to build it on top of the relational PostgreSQL database
https://blog.timescale.com/time-series-data-why-and-how-to-use-a-relational-database-instead-of-nosql-d0cd6975e87c
Related
I have a kind of requirement but not able to figure out how can I solve it. I have datasets in below format
id, atime, grade
123, time1, A
241, time2, B
123, time3, C
or if I put in list format:
[[123,time1,A],[124,timeb,C],[123,timec,C],[143,timed,D],[423,timee,P].......]
Now my use-case is to perform comparison, aggregation and queries over multiple row like
time difference between last 2 rows where id=123
time difference between last 2 rows where id=123&GradeA
Time difference between first, 3rd, 5th and latest one
all data (or last 10 records for particular id) should be easily accessible.
Also need to further do compute. What format should I chose for dataset
and what database/tools should I use?
I don't Relational Database is useful here. I am not able to solve it with Solr/Elastic if you have any ideas, please give a brief.Or any other tool Spark, hadoop, cassandra any heads?
I am trying out things but any help is appreciated.
Choosing the right technology is highly dependent on things related to your SLA. things like how much can your query have latency? what are your query types? is your data categorized as big data or not? Is data updateable? Do we expect late events? Do we need historical data in the future or we can use techniques like rollup? and things like that. To clarify my answer, probably by using window functions you can solve your problems. For example, you can store your data on any of the tools you mentioned and by using the Presto SQL engine you can query and get your desired result. But not all of them are optimal. Furthermore, usually, these kinds of problems can not be solved with a single tool. A set of tools can cover all requirements.
tl;dr. In the below text we don't find a solution. It introduces a way to think about data modeling and choosing tools.
Let me take try to model the problem to choose a single tool. I assume your data is not updatable, you need a low latency response time, we don't expect any late event and we face a large volume data stream that must be saved as raw data.
Based on the first and second requirements, it's crucial to have random access (it seems you wanna query on a particular ID), so solutions like parquet or ORC files are not a good choice.
Based on the last requirement, data must be partitioned based on the ID. Both the first and second requirements and the last requirement, count on ID as an identifier part and it seems there is nothing like join and global ordering based on other fields like time. So we can choose ID as the partitioner (physical or logical) and atime as the cluster part; For each ID, events are ordered based on the time.
The third requirement is a bit vague. You wanna result on all data? or for each ID?
For computing the first three conditions, we need a tool that supports window functions.
Based on the mentioned notes, it seems we should choose a tool that has good support for random access queries. Tools like Cassandra, Postgres, Druid, MongoDB, and ElasticSearch are things that currently I can remember them. Let's check them:
Cassandra: It's great on response time on random access queries, can handle a huge amount of data easily, and does not have a single point of failure. But sadly it does not support window functions. Also, you should carefully design your data model and it seems it's not a good tool that we can choose (because of future need for raw data). We can bypass some of these limitations by using Spark alongside Cassandra, but for now, we prefer to avoid adding a new tool to our stack.
Postgres: It's great on random access queries and indexed columns. It supports window functions. We can shard data (horizontal partitioning) across multiple servers (and by choosing ID as the shard key, we can have data locality on computations). But there is a problem: ID is not unique; so we can not choose ID as the primary key and we face some problems with random access (We can choose the ID and atime columns (as a timestamp column) as a compound primary key, but it does not save us).
Druid: It's a great OLAP tool. Based on the storing manner (segment files) that Druid follows, by choosing the right data model, you can have analytic queries on a huge volume of data in sub-seconds. It does not support window functions, but with rollup and some other functions (like EARLIEST), we can answer our questions. But by using rollup, we lose raw data and we need them.
MongoDB: It supports random access queries and sharding. Also, we can have some type of window function on its computing framework and we can define some sort of pipelines for doing aggregations. It supports capped collections and we can use it to store the last 10 events for each ID if the cardinality of the ID column is not high. It seems this tool can cover all of our requirements.
ElasticSearch: It's great on random access, maybe the greatest. With some kind of filter aggregations, we can have a type of window function. It can handle a large amount of data with sharding. But its query language is hard. I can imagine we can answer the first and second questions with ES, but for now, I can't make a query in my mind. It takes time to find the right solution with it.
So it seems MongoDB and ElasticSearch can answer our requirements, but there is a lot of 'if's on the way. I think we can't find a straightforward solution with a single tool. Maybe we should choose multiple tools and use techniques like duplicating data to find an optimal solution.
We are looking into using Azure Table Storage (ATS) together with Deedle (or other libraries with similar functionality) for our time-series storage, manipulations and calculations. From what I can read, F# also seems like a good choice for operations on arrays.
Our starting point is a set of time-series for energy consumption. The series will either be the consumption within an interval (fixed or irregular intervals) or a counter (from which we can calculate the consumption from one reading to the next). As a data point is just a tag (used as a partition key), timestamp (rowkey) and value, this should be well suited for ATS.
From a user's perspective, they want to do calculations on the series for a given period and resolution, e.g. calculate a third series as a difference between two others, for one given year with monthly resolution.
This raises a number of questions:
Will ATS together with F# be fast enough? If we have 10.000 data points? 100.000? Compared to C#?
Resampling will require calculations of points between the series' timestamps. I haven't seen any Deedle examples for (linear) interpolation, but I assume that this is just passing a function which can look at the necessary data points? Will this be fast enough for our number of points?
The calculations will be determined by the users and we must have this as configurations. My best guess so far is to have the formula in some format we can parse easily into reverse polish notation, and take special care of tags that will represent series (ie. read from ATS, resample, then do the operations).
Any comments will be highly appreciated!
I think Isaac already mentioned the most important points, but as this question involves some of the things I'm involved with, I thought I'd share a few additional remarks!
BigDeedle. As Isaac mentioned, I used Azure Table storage in BigDeedle. This is mainly useful if you want to explore data interactively using Deedle APIs and do some filtering and range restriction before getting the data in memory and running your calculations. BigDeedle loads data lazily from potentially very big external data source. That said, if you eventually need to load all data into memory, this might not be all that useful for you.
The storage model used in BigDeedle might be useful though - it partitions data based on date, so when you want to get values in a given date range, it knows in which partitions to look. In my experience, loading data from ATS works pretty well, especially if you can do it on an MBrace cluster running in Azure (which is what my NDC demo does in the end).
Efficiency. I think the combination should work well for 10k or 100k data points - there will be no difference whether you do this from F# or C#. As for Deedle, I've definitely used it with data sets of this size - we optimize the library "as needed". Most of the functions are quite efficient already, but there may be some operations that are not efficient. This is something that can be fixed if you open issue on GitHub.
Resampling. There is built-in function for linear interpolation (see here), but I suspect you may need to write your own custom interpolation. Deedle does not "hide the underlying data" from you, so this is not too hard - the last example on this page shows a custom function for filling missing data that uses linear interpolation. If you are doing something like this, you'll need to have the data in memory (so BigDeedle would not be very useful here).
Specifying calculations. I suspect this is a separate question, but F# is great for domain-specific languages. I did a talk on that at earlier NDC. Generally, you can either specify your own DSL (and parse it) or have an embedded DSL where people write subset of F#. F# has good support for both.
PS: If you wanted to get some more help with F#, Deedle and Azure tables, feel free to get in touch. I'm happy to share my experience - you should be able to find a contact via my profile.
F# versus C# will probably be basically the same perf wise unless you do something completely different between the two (for example, immutable vs mutable data sets). Both compile down to IL at the end of the day.
Azure Table Storage - make sure you pick your partition + row keys correctly. There is a lot of documentation on picking Azure Table Storage partition keys, especially over time series - make sure you group rows up at the correct level to ensure data is distributed, with partitions not too large or small. You might also want to look at the Azure Storage Type Provider and / or Azure Storage F# libraries which makes working with ATS easier than the standard .NET SDK.
Deedle AFAIK does indeed have ability to replace missing values across time series, and there's at least a project called BigDeedle which works directly over ATS (although I'm not sure how ready this project is).
We're investigating options to store and read a lot of immutable data (events) and I'd like some feedback on whether Cassandra would be a good fit.
Requirements:
We need to store about 10 events per seconds (but the rate will increase). Each event is small, about 1 Kb.
A really important requirement is that we need to be able to replay all events in order. For us it would be fine to read all data in insertion order (like a table scan) so an explicit sort might not be necessary.
Querying the data in any other way is not a prime concern and since Cassandra is a schema db I don't suppose it's possible when the events come in many different forms? Would Cassandra be a good fit for this? If so is there something one should be aware of?
I've had the exact same requirements for a "project" (rather a tool) a year ago, and I used Cassandra and I didn't regret. In general it fits very well. You can fit quite a lot of data in a Cassandra cluster and the performance is impressive (although you might need tweaking) and the natural ordering is a nice thing to have.
Rather than expressing the benefits of using it, I'll rather concentrate on possible pitfalls you might not consider before starting.
You have to think about your schema. The data is naturally ordered within one row by the clustering key, in your case it will be the timestamp. However, you cannot order data between different rows. They might be ordered after the query, but it is not guaranteed in any way so don't think about it. There was some kind of way to write a query before 2.1 I believe (using order by and disabling paging and allowing filtering) but that introduced bad performance and I don't think it is even possible now. So you should order data between rows on your querying side.
This might be an issue if you have multiple variable types (such as temperature and pressure) that have to be replayed at the same time, and you put them in different rows. You have to get those rows with different variable types, then do your resorting on the querying side. Another way to do it is to put all variable types in one row, but than filtering for only a subset is an issue to solve.
Rowlength is limited to 2 billion elements, and although that seems a lot, it really is not unreachable with time series data. Especially because you don't want to get near those two billions, keep it lower in hundreds of millions maximum. If you put some parameter on which you will split the rows (some increasing index or rounding by day/month/year) you will have to implement that in your query logic as well.
Experiment with your queries first on a dummy example. You cannot arbitrarily use <, > or = in queries. There are specific rules in SQL with filtering, or using the WHERE clause..
All in all these things might seem important, but they are really not too much of a hassle when you get to know Cassandra a bit. I'm underlining them just to give you a heads up. If something is not logical at first just fall back to understanding why it is like that and the whole theory about data distribution and the ring topology.
Don't expect too much from the collections within the columns, their length is limited to ~65000 elements.
Don't fall into the misconception that batched statements are faster (this one is a classic :) )
Based on the requirements you expressed, Cassandra could be a good fit as it's a write-optimized data store. Timeseries are quite a common pattern and you can define a clustering order, for example, on the timestamp of the events in order to retrieve all the events in time order. I've found this article on Datastax Academy very useful when wanted to learn about time series.
Variable data structure it's not a problem: you can store the data in a BLOB, then parse it internally from your application (i.e. store it as JSON and read it in your model), or you could even store the data in a map, although collections in Cassandra have some caveats that it's good to be aware of. Here you can find docs about collections in Cassandra 2.0/2.1.
Cassandra is quite different from a SQL database, and although CQL has some similarities there are fundamental differences in usage patterns. It's very important to know how Cassandra works and how to model your data in order to pursue efficiency - a great article from Datastax explains the basics of data modelling.
In a nutshell: Cassandra may be a good fit for you, but before using it take some time to understand its internals as it could be a bad beast if you use it poorly.
There is a requirement to keep a list of top-10 localities in a city from where demand for our food service is emanating at any given instant. The city could have tens of thousands of localities.
If one has to make a near real time (lag no more than 5 minutes) datastore in memory that would
- keep count of incoming demand by locality (geo hash)
- reads by hundreds of our suppliers every minute (the ajax refresh is every minute)
I was thinking of a multi threaded synchronized max-heap. This would be a complex solution as tree locking is by itself a complex implementation.
Any recommendations for the best in-memory (replicatable master slave) data structure that can be read and updated in multi threaded environment?
We expect 10K QPS and 100K updates per second. When we scale to other cities and regions, we will need per city implementation of top-10.
Are there any off the shelf solutions available?
Persistence is not a need so no mySQL based solutions. If you recommend redis or mongo DB solution, please realize that the queries are not pointed-queries by key but a top-N query instead.
Thanks in advance.
If you're looking for exactly what you're describing, there are a few approaches that might work nicely. There are several papers describing concurrent data structures that could work as priority queues; here is one option that I'm not super familiar with but which looks promising. You might also want to check out concurrent skip lists, which should also match your requirements.
If I'm interpreting your problem statement correctly, you're hoping to maintain a top-10 list of locations based on the number of hits you receive. If that's the case, I would suspect that while the number of updates would be huge, the number of times that two locations would switch positions would not actually be all that large. In other words, most updates wouldn't actually require the data structure to change shape. Consequently, you could consider using a standard binary heap where each element uses an atomic-compare-and-set integer key and where you have some kind of locking system that's used only in the case where you need to add, move, or delete an element from the heap.
Given the scale that you're working at, you may also want to consider approximate solutions to your problem. The count-min sketch data structure, for example, was specifically designed to estimate frequent elements in a data stream and does so extremely quickly. It can easily be distributed and linked up with a priority queue in a manner similar to what I described above. There are lots of good implementations out there, and if I remember correctly this data structure is actually deployed in situations like the one you're describing.
Hope this helps!
Hi all: If I am storing News articles in a DB with different categories such as "Tech", "Finance", and "Health", would a distributed database work well in this system vs a RDBMS? Each of the news items would have the news articles attached as well as a few other items. I am wondering if querying would be faster, though.
Let's say I never have more than a million rows, and I want to grab the latest (within 5 hours) tech articles. I imagine that would be a map-reduce of "Give me all tech articles" (possibly 10000), then weed out only the ones that have the latest timestamp.
Am I thinking about tackling the problem in the right way, and would a DDB even be the best solution? In a few years there might be 5 million items, but even then....
Whether to use a distributed database or key-value store depends more on your operational requirements than your domain problem.
When people ask how to do time-ordered queries in Riak, we usually suggest several strategies (although none of them are a silver-bullet as Riak lacks ordered range queries):
1) If you are frequently accessing a specifically-sized chunk of time, break your data into buckets that reflect that period. For example, all data for the day, hour or minute specified would be either stored or linked to from a bucket that contains the appropriate timestamp. If I wanted all the tech news from today, the bucket name might be "tech-20100616". As your data comes in, add appropriate links from the time-boxed bucket to the actual item.
2) If the data is more sequence-oriented and not related to a specific point in time, use links to create a chain of data, linking backward in time, forward, or both. (This works well for versioned data too, like wiki pages.) You might also have to keep an object that just points at the head of the list.
Those strategies aside, Riak is probably not the 100% solution for up-to-the-minute information, but might be better for the longer-term storage. You could combine it with something like Redis, memcached, or even MongoDB (which has great performance if your data is mildly transient and can fit in memory) to hold a rolling index of the latest stuff.