I am working on a system where I need fast filtering queries. Basically, it is a set of 50 different fields, booleans, amounts, code and dates; just like a web-shop filter.
it is ~ 10 000 000 items.
For the moment I am using MSSQL, and using one big table with different indexes except for a few separate tables when I found it much faster to join instead of just filter the result in one table.
I usually get a response time around 1 second, with a fairly fast server.
I was considering to use ArangoDB for this and wonder what approach is best? Is it better to keep some of the "flags" as separate tables and join or is it more efficient to put everything in the same document and have it as a flag with an index? Or would it be any benefit using the graph/edge feature and make a link back to the same object (or an object representing the code for instance)?
The reason I am considering ArangoDB is that my plan is to have a more complex model and will most likely use the graph feature in the future even if the first priority is to get the system up to the current level of features with a similar speed.
Any thoughts?
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.
I am stumped at what seems to be a fundamental problem with Elasticsearch and polymorphic data. I would like to be able to find multiple types of results (e.g. users and videos and playlists) with just one Elasticsearch query. It has to be just one query, since that way Elasticsearch can do all the scoring and I won't have to do any magic to combine multiple query results of different types.
I know that Elasticsearch uses a flat document structure, bringing me to the following problem. If I index polymorphic data, I will have to specify a 'missing' value for each unique attribute that I care about in scoring subtypes of the polymorphic data.
I've looked for examples of other dealing with this problem and couldn't find any. There doesn't seem to be anything in the documentation on this either. Am I overlooking something obvious or was Elasticsearch just not designed to do something like this?
Kind regards,
Steffan
Thats not the issue of Elasticsearch itself, its the problem (or limitation) of underlying lucene indexes. So, any db/engine based on lucene will have the same problems (if not worse :), ES does a hell ton of job for you). Probably, ES will ease the pain in further releases, but not dramatically. And IMO, there's hardly any hi-perf search engine that can bear with true polymorphic data.
The answer depends on your data structure, thats for sure. Basically, you have two options:
Put all your data in single index, and split it by types. And you already know the overhead - lucent indexes works poorly with sparse data. More similar your data is, less problem you have. Anyway, ES will do all the underlying job for "missing" values, you only have to cope with memory/disk overhead for storing sparse data.
If your data is organised with parent-child relation (i.e. video -> playlist), you definitely need single index for such data. Which is leaving you with this approach only.
Divide your data into multiple indexes. This way you have slightly higher disk overhead for lucene index + possibly higher CPU usage when aggregation data from multiple shards (so, you should tune your sharding respectively).
You still can query ES for all your documents in single request, as ES supports multi-index queries.
So, this looks like question purely of your data structure. I'd recommend to simply fire up small cluster to measure memory/disk/cpu usage for expected data. More details on "index vs shard" – great article by Adrien.
Slightly off-topic, if ES doesn't seem to feet your needs, I suggest you to
still consider merging data on application side. ES works great with multiple light request (instead of few heavier), and as your results from ES is sorted already, you need to merge sorted streams having sorted input. Not so much magic there, tbh.
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.
I'm using Cognos 10.1 and I have a report that uses two queries each with the same primary key.
Query 1: UniqueIds
Query 2: DetailedInfo
I'm not sure how to tell whether it's better build a report using the DetailedInfo query with a filter that says PrimaryKey in (UniqueIds.PrimaryKey) or should I create a third query that joins UniqueIds to DetailedInfo on PrimaryKey.
I'm new to Cognos and I'm learning to think differently. Using MicroSoft SQL Server I'd just use an inner join.
So my question is, in Cognos 10.1 which way is better and how can tell what the performance differences are?
You'd better start from the beginning.
You queries (I hope Query Subjects) should be joined in Framework Manager, in a model. Then you can easily filter second query by applying filters to first query.
Joins in Report Studio is the last solution.
The report writers ultimate weapon is a well indexed data warehouse, with a solid framework model built on top.
You want all of your filtering and joining to happen on the database side as much as possible. If not, then large data sets are brought over to the Cognos server before they are joined and filtered by Cognos.
The more work that happens on the database, the faster your reports will be. By building your reports in certain ways, you can mitigate Cognos side processing, and promote database side processing.
The first and best way to do this is with a good Framework Model, as Alexey pointed out. This will allow your reports to be simpler, and pushes most of the work to the database.
However a good model still exposes table keys to report authors so that they can have the flexibility to create unique data sets. Not every report warrants a new Star Schema, and sometimes you want to join the results of queries against two different Star Schema sources.
When using a join or a filter, Cognos attempts to push all of the work to the database as a default. It wants to have the final data set sent to it, and nothing else.
However when creating your filters, you have two ways of defining variables... with explicit names that refer to modeled data sources (ie. [Presentation View].[Sales].[Sales Detail].[Net Profit] ) or by referring to a column in the current data set (such as [Net Profit] ). Using explicit columns from the model will help ensure the filters are applied at the database.
Sometimes that is not possible, such as with a calculated column. For example, if you dont have Net Profit in your database or within your model, you may establish it with a Calculated column. If you filter on [Net Profit] > 1000, Cognos will pull the dataset into Cognos before applying your filter. Your final result will be the same, but depending on the size of data before and after the filter is applied, you could see a performance decrease.
It is possible to have nested queries within your report, and cognos will generate a single giant SQL statement for the highest level query, which includes sub queries for all the lower level data. You can generate SQL/MDX in order to see how Cognos is building the queries.
Also, try experimenting. Save your report with a new name, try it one way and time it. Run it a few times and take an average execution speed. Time it again with the alternate method and compare.
With smaller data sets, you are unlikely to see any difference. The larger your data set gets, the bigger a difference your method will affect the report speed.
Use joins to merge two queries together so that columns from both queries can be used in the report. Use IN() syntax if your only desire is to filter one query using the existence of corresponding rows in a second. That said, there are likely to be many cases that both methods will be equally performant, depending on the number of rows involved, indexes etc.
By the way, within a report Cognos only supports joins and unions between different queries. You can reference other queries directly in filters even without an established relationship but I've seen quirks with this, like it works when run interactively but not scheduled or exported. I would avoid doing this in reports.
I am developing an Azure based website and I want to provide search capabilities using Lucene. (structured json objects would be indexed and stored in Lucene and other content such as Word documents, etc. would be indexed in lucene but stored in blob storage) I want the search to be secure, such that one user would never see a document belonging to another user. I want to allow ad-hoc searches as typed by the user. Lastly, I want to query programmatically to return predefined sets of data, such as "all notes for user X". I think I understand how to add properties to each document to achieve these 3 objectives. (I am listing them here so if anyone is kind enough to answer, they will have better idea of what I am trying to do)
My questions revolve around performance and security.
Can I improve document security by having a separate index for each user, or is including the user's ID as a parameter in each search sufficient?
Can I improve indexing speed and total throughput of the system by having a separate index for each user? My thinking is that having separate indexes would allow me to scale the system by having multiple index writers (perhaps even on different server instances) working at the same time, each on their own index.
Any insight would be greatly appreciated.
Regards,
Nate
Of course, one index.
You can do even better than what you suggested by using ManifoldCF (Apache product that knows how to handle Solr) to manage security.
And one off topic, uninformed suggestion: I'd rather use CloudBees or Heroku (or Amazon) instead of Azure.
Until you will use several machines for indexing I guess it's more convenient to use single index. Lucene community done a lot of work to make indexing process as efficient as it can. So unless you intentionally want to implement distributed indexing I doesn't recommend you to split indexes.
However there are several reasons why you would want to split indexes:
if your machine have several IO devices which could be utilized in parallel. In this case, if you are IO bound, splitting indexes is good idea.
splitting document fields between indexes (this is what ParallelReader is supposed for). This is more exotic form of splitting, but it may be a good idea if search is performed using different groups of fields. Suppose, we have two search query types: the first is using field name and type, and the second is using fields price and discount. If those fields are updated at different rate (I guess, name updates are far more rarely than price updates), updating only part of index would require less IO resources. This will give more overall throughput to the system.