This second method is one that MongoDB users and the company itself recommends when it comes to time-series, which we call Mongo-recommended. The engineering idea behind this method is clever: create a document for each device, for each hour, which contains a 60 by 60 matrix representing every second of every minute in that hour. Would like to hear your thoughts on your experience with TSDBs and what solutions you are looking to build on top of them. and cannot match the timeField required by timeseries collections. This is the first approach that would come to mind for most developers when trying to store time-series data in MongoDB, hence the name "naive". The granularity parameter represents a string with the following options: Granularity should be set to the unit that is closest to rate of ingestion for a unique metaField value. Note: This is in no way a performance test nor tuned to extract the best performance. The first complex query (lastpoint) finds the latest reading for every device in the dataset. MongoDB is among the best-known NoSQL databases, emerging at the end of the last decade to become the face of NoSQL and the foundation of a nearly $21 billion company (as of writing). As shown in the above example, data in a time series database has a timestamp and at least one metric related to it. Time series databases are specifically designed for time series data management. Is there any evidence suggesting or refuting that Russian officials knowingly lied that Russia was not going to attack Ukraine? MongoDB, Inc. (NASDAQ:NASDAQ:MDB) Q1 2024 Earnings Call Transcript June 1, 2023 5:00 PM ETCompany ParticipantsBrian Denyeau - Investor Relations, ICRDev Ittycheria - President & Chief. Time series data often grows at very high rates and becomes less useful as it ages. The best way to benchmark read latency is to do it with the actual queries you plan to execute. How should I store time series in mongodb Ask Question Asked 11 years, 2 months ago Modified 7 years, 11 months ago Viewed 19k times 13 I need to create a database of time series, and perform the following tasks: create new time series update existing time series Either the Max or Average over a window or perhaps just every kth sample will be plotted. Is "different coloured socks" not correct? You can create secondary indexes as required. Sometimes time-series data will come into your database at high frequency - use-cases like financial transactions, stock market data, readings from smart meters, or metrics from services you're hosting over hundreds or even thousands of servers. We are able to do some clever query construction in both to get a list of distinct devices which allows both setups to stop searching when every device has a point associated with it. Once you create the time series collection, insert works like any other collection. Two things that jump out besides the verbose JSON syntax: In this example the SQL query is far shorter and simpler, making it easier to comprehend and debug. Data can have many dimensions. In a day there are 24 hours. First, for efficiently stopping the query, the client running the query will have to compute the subset of documents to look in, which creates the lengthy list in the first $match aggregator below. Would sending audio fragments over a phone call be considered a form of cryptology? However, when aggregating one or more metrics across multiple devices for multiple hours, TimescaleDB shows between 208% and 302% the performance of MongoDB. But if you want even better performance, you may want to continue reading.). MongoDB provides the following mechanisms. Notice that these queries take in the order of 10s of seconds (rather than milliseconds), so a 13-21x performance gain is very noticeable. For double rollups aggregating metrics by time and another dimension (e.g., GROUPBY time, deviceId), TimescaleDB shows large gains. Find centralized, trusted content and collaborate around the technologies you use most. Instance size: Both client and database server ran on DigitalOcean virtual machines (droplets) with 32 vCPU and 192GB Memory each. This document is then updated each time a new reading comes in, rather than doing a new document insert: This method makes it possible to do some efficient filtering when it comes to queries, but comes with a more cumbersome implementation and decreased (albeit not terrible) write performance. For the more complex lastpoint query, TimescaleDB shows 5399% (or 54x) the performance of MongoDB. I am curious to know if mongodb naturally stores results in the order of being recorded? From the very beginning, developers have been using MongoDB to store time-series data. And, if there were a clear winner between the two methods for simple queries, we could save ourselves some time by not implementing our full query set against both methods. While unpacking, only the required measurements are retrieved and not the entire set. This means that there is a slight chance of misalignment in the timestamps. Storing of data in insertion order is only guaranteed for capped collections I'm afraid. If query performance is your most important requirement, TimescaleDB is the clear choice for both simple and complex queries. Time series collections only store meta data once. Time series data always has one of the axes as time. Lets unpack the results for each query type below: For simple rollups (i.e., groupbys), when aggregating one metric across a single host for 1 or 12 hours, or multiple metrics across one or multiple hosts (either for 1 hour or 12 hours), TimescaleDB performs comparably to or outperforms MongoDB. Yet, thats not even the worst result! Before diving into write and read performance numbers, lets take a moment to examine in more detail the two methods we evaluated for storing time-series data in MongoDB. Metrics data about a server, IoT sensor data, eCommerce data, log data and other could run into billions and trillions of data points over a period of time. Based on the granularitymultiple buckets are automatically created for storing data. The second method achieves good query performance on simple queries, but has worse write performance, higher implementation complexity, and fails to deliver good query performance on more complex time-series queries when compared to TimescaleDB. However, while MongoDB does support JOINs, they are not as natural to work with or "feature-full" as they are for relational databases like TimescaleDB. Time series data is queried based on time to data for a period of time. And, as we've shown, when it comes to time-series workloads, TimescaleDB - a purpose-built time-series database - delivers significantly better results on every dimension. When using sharded time series collections, you cannot modify the granularity of a sharded time series collection. Given this significant difference in query performance compared to the more modest different in write performance (a loss of 20% for Mongo-recommended), we decided that the recommended approach from MongoDB and others was indeed probably the best setup for time-series data. Ultimately that poor performance makes it an impractical setup for time-series data overall. This capability fulfils a very specific need to optimally store and operate on time series data. We decided to evaluate for ourselves to answer that question, even trying multiple methods to make sure we were fair to MongoDB. Not necessarily but there are enough blogs, talks, and other material out there about using MongoDB for time-series data that we felt we needed to do an evaluation. Cyclical fluctuations: These are time series variations for more than a year. If you're weighing your options, based on our analysis, TimescaleDB is the clear choice. This pattern is actually needed for almost all of the queries we looked at here, which makes all the queries verbose and potentially daunting to debug. Imagine you are storing data every minute. Further, this method eats up disk space, using up nearly 50% more than method 2. But what if we wanted to store the entire day, and not just the entire hours, in one document. And heres that same query expressed in MongoDB. Date can be stored in various formats using the DateTime data type. The data will be plotted using flot. With a first glance at the data, the full resolution is not required, but if need be you could zoom in. This document is then updated each time a new reading comes in, rather than doing a new document insert: This method does make it possible to do some efficient filtering when it comes to queries, but comes with a more cumbersome implementation and decreased (albeit not terrible) write performance. You can categorize them into legacy database players who built time series capabilities on top of their existing engine and niche players that have purpose built time series database engines. rev2023.6.2.43474. If you just dump each reading into a new document, youre in for bad time when the data accumulates and you want to start querying it. Thus, for the remainder of this post and our analysis, we use the Mongo-recommended setup whenever benchmarking MongoDB. The above examples are of linear time series data, where each point can be viewed as the linear combination between past, present, and future data, and can be analyzed using regression, auto-correlation, and other methods. TSDBs are optimized for high high rate of data ingestion, higher compression% and low latency retrieval of data for time series data analysis. http://www.mongodb.org/display/DOCS/Capped+Collections, Time series data & MongoDB | Introduction, Querying analyzing & presenting time series data, Building a safer community: Announcing our new Code of Conduct, Balancing a PhD program with a startup career (Ep. Two metadata fields with the same contents but different order are considered to be identical. Atlas online archive feature allows you to move data to cold tier storage for more cost efficient storage. These functions can be applied to, expireAfterSeconds configured for time series collections allows MongoDB to automatically expire older documents. MongoDB allows you to store and process time series data at scale. When aggregating 10 metrics, Timescale showed 1327% (or 13x) the performance of MongoDB. In this tutorial, we will show you how to build a C++ console application that uses MongoDB to store time series data, related to the Air Quality Index (AQI) for a given location. However, with digitization and the popularity of smart devices, the use cases of time series databases have gone up. Expired documents are automatically purged by MongoDB. Creating a time series collection is straightforward, all it takes is a field in your data that corresponds to time, just pass the new "timeseries'' field to the createCollection command and youre off and running. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Time series data is time-stamped data arranged as a sequence of data points indexed in the order of timefor example, daily petrol price, average monthly wages of employees, hourly Facebook logins, etc. TimescaleDB delivers 260% higher insert performance, up to 53x faster queries, and better developer experience vs. MongoDB. Basically, a capped collection has a specified size, and documents are written to it in insertion order until it fills up, at which point it wraps around and begins overwriting the oldest documents with the newest. There are three aspects of a time series database: database features, time series features, and data features. However, while MongoDB does support JOINs, they are not the bread-and-butter that they are for relational databases like TimescaleDB: Ouch. All three setups do achieve write performance that makes them suitable for time-series data, but Mongo-naive and TimescaleDB certainly stand a cut above. Seasonal variations: These are regular periodic variations observed during one yearfor example, the sale of geysers and air coolers, and the number of weddings in a particular period. A time series database should satisfy the following requirements: MongoDBs data platform provides all of these features and is quite suitable for handling time series data. Before we compared MongoDB against TimescaleDB, we first considered the query performance between the two MongoDB methods. More specifically, compared to MongoDB, TimescaleDB exhibits: Create a free account to get started with a fully-managed TimescaleDB instance (100% free for 30 days). (For anyone who wants to re-run these benchmarks at home, please note that we will be open sourcing the benchmarking code in the next several weeks. I also intend to look into the "New Aggregation Framework" in place of Map reduce. Finally, this approach limits the granularity of your data. That is 12 points. Timestamp supports calendar and time zone adaptation. You can design your document models more intuitively, the way you would with other types of MongoDB collections. Further, while queries for this method are typically more performant, designing the query in the first place requires more effort, especially when reasoning about which aggregate documents can be filtered/pruned. While single rollups are pretty comparable across the two systems, other more complex queries are not. Enter MongoDB. As we can see, not only is TimescaleDB much more performant, but the query language (i.e., SQL) is also much simpler and easier to read (a crucial criteria for sustainable software development): And heres that same query expressed in MongoDB. (Previously we compared TimescaleDB to plain PostgreSQL.) What happens when you query a time series collection? You can only set a collections timeField and metaField parameters when creating the collection. I'll just index the time stamps and tolerate the speed of map reduce. You can access MongoDB data from anywhere using MongoDB Atlas, MongoDBs cloud-based application data platform. To make a range search based on a different timezone, I would use moment.js to do the conversion from the local time into UTC. Cyclic variations form a complete circle and return to the start state, with oscillationsfor example, business cycles and weather cycles. $fill will fill in the missing data points. Timestamp as Key in mongodb as timeseries database, MongoDB - range queries on time series subdocuments. Of course that may be true, but there are so many more reasons to use the new time series collections over regular collections for time-series data. Please say hi! Data summary and reporting: Using time series features of a TSD, you can get a summary of data for different times in a more optimized way. Time series collections need ability to automatically archive old data or purge them when not needed. The advantage of using time series collections can be summarized into the following: Now time series collection is not a silver bullet to solve all problems. MongoDB $avg provides Simple Moving Average of the series of data. In this case, you produce one document per value recorded, which causes a lot of insert operations. Connect and share knowledge within a single location that is structured and easy to search. We'll be covering this in a later post, but in the meantime, you should check out the official documentation for a list of migration tools and examples. But even if youre only interested in storing time-series data in MongoDB, this post presents two different ways of doing so, depending on what you want to optimize for. The sensors are relatively similar, some of them have different sample rates and are recorded by different machines. If you wanted the previous 100 seconds you could add .skip(100000). While we know some of these limitations may be impactful to your current use case, we promise we're working on this right now and would love for you to provide your feedback! There are a number of TSDBs available in the market. The sluggishness of the Mongo-recommended methods ingest rate is likely due to the extra cost involved in occasionally creating new, larger documents (e.g., when a new hour or device is encountered). Second, to unpack the 60x60 matrices in each document, the $unwind/$project/$unwind pattern is needed to efficiently expand those matrices while removing empty time periods. For example, to efficiently manage writes, the database must keep a client-side cache of which documents are already made so that a more costly upsert (i.e., insert if it doesnt exist, otherwise update) pattern is not needed. This pattern is actually needed for almost all of the queries we looked at here, which makes all the queries verbose and potentially daunting to debug. Note, this approach was documented here and here. So for our given use case (see setup below) of monitoring CPU metrics, the JSON document looks like this: Conceptually and in implementation, this method is very simple, so it seems like a tempting route to go: batch all measurements that occur at the same time into one document along with their associated tags, and store them as one document. In this example, an identifying ID and location for a sensor collecting weather data. The database should be able to handle large amounts of writes, and reads/updates should be at particular time windows. I'm trying to use mongodb for a time series database and was wondering if anyone could suggest how best to set it up for that scenario. We decided to evaluate it for ourselves, with the obvious caveat that we are the creators of a competing product. MongoDB grew in popularity as a simple document store for quickly prototyping and easily scaling web apps. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Time series databases (TSDBs) are optimized for storing and retrieving large volumes time series data. If your insert performance is far below these benchmarks (e.g., if it is 2,000 rows / second), then insert performance will not be your bottleneck. Specifying an appropriate value allows the time series collection to be optimized for your usage. While this is just an example, your document can look like nearly anything. For example I created the following secondary indexes to be able to search by just server.id or search by server.region. Relational and non-relational databases have timestamp data types to store time-related data. As a result we find that our write performance is actually comparable to MongoDB at its fastest (Mongo-naive), as shown in the figure below. In particular, we evaluated two methods of using MongoDB as a time series database: (1) a naive, document-per-event method and (2) a method recommended by MongoDB users (and MongoDB itself) that aggregates events into hourly documents. C++ MongoDB Time series Feb 10, 2023 Rishabh Bisht Tutorial IoT (Internet of Things): Smart home and wearable devices, mobile phones, and inventory management systems keep track of every activity and keep sending data for generating alerts and patterns to track usage and set goals. However, when it comes to time-series data, it isnt all about frequency, the only thing that truly matters is the presence of time so whether your data comes every second, every 5 minutes, or every hour isnt important for using MongoDB for storing and working with time-series data. Internally the data is represented as follows. The order of metadata fields is ignored in order to accommodate drivers and applications representing objects as unordered maps. Measurements with a common metaField for periods of time will be grouped together internally to eliminate the duplication of this field at the storage layer. If youd like to re-run these benchmarks yourself or compare other time-series databases like InfluxDB vs MongoDB, you can do so using the open-source Time Series Benchmarking Suite. Whereas normal collections use snappy as default. That said, we recommend doing an honest analysis of your insert needs. For example, the diesel price was $5.45 (metric) on 24-04-2022. For complex queries, which are commonly used to analyze and monitor devices for DevOps and IoT use cases, TimescaleDB again vastly outperforms MongoDB, showing up to 53x better performance. In this blog, I look at typical characteristics of time series data, what are the requirements from a database system that can handle time series data as well as a summary of my experiments with various capabilities that MongoDB provides to meet these requirements. Iterate the array and and for each array element accumulate the amount value (you have to search for matching hours). So we first compared the two MongoDB methods using 3 single rollup (groupby) queries (on time) and one double rollup query (on time and device hostname). Time series data is generally composed of these components: Time when the data point was recorded. In a future post we will discuss ways to automatically archive your data and efficiently read data stored in multiple locations for long periods of time using MongoDB Online Archive. Time series data is the collection of data that is queried and indexed based on time-period. Because the data in our evaluation was at the granularity of seconds, not milliseconds, and given the query performance we saw (as detailed in the next section), we ultimately decided that this method is probably the best method for comparison against TimescaleDB. Measurements are stored in columnar fashion, There is a control block that has min and max values readily available, Data inserted through a Java program running on a VM. TSD also allows for data mining as it can scale, and huge amounts of data can be stored as the requirement grows. The metaField typically does not change over time. I can't see this being a problem for what you describe. MongoDB can be an extremely efficient engine for storing and processing time-series data, but you'd have to know how to correctly model it to have a performant solution, but that wasn't as straightforward as it could have been. By analyzing past and current time series data, businesses can determine metrics, find patterns and trends, and forecast business performance for a period of time. The first method, which well refer to as Mongo-naive, is straightforward: each time-series reading is stored as a document. Time series data are used in numerous use-cases ranging from predicting weather, heights of tides, stock prices to financial fraud. All three setups achieve write performance of greater than 1 million metrics per second. We have seen how time series collections work better than normal collections in specific niche areas of processing time series data in IoT, eCommerce, finance and other domains. This resulted in TimescaleDB showing 2108 % (or 21x) the performance of MongoDB. If we build the documents with all the values filled-in with padding in advance, we can be sure that the document will not change size and therefore will not be moved. Would it be possible to build a powerless holographic projector? TimescaleDB is between 46x faster than MongoDB in these cases. Users will always be able to work with the abstraction layer and not with a complicated compressed bucketed document. Time series collections use zstd as default compression algorithm. Moreover, implementing MongoDB's recommended time-series approach requires writing client-side code and using fairly verbose queries. You also notice the namespace is different from the collection you queried. Time Series Secondary Indexes with MongoDB 5.0 and Earlier Capped Collections Modification of Collection Type Modification of timeField and metaField Modification of granularity Sharding Sharding Administration Commands Shard Key Fields Resharding Transactions View Limitations This page describes limitations on using time series collections. Despite being implemented in a different way from the collections you've used before, to optimize for time-stamped documents, it's important to remember that you can still use the MongoDB features you know and love, including things like nesting data within documents, secondary indexes, and the full breadth of analytics and data transformation functions within the aggregation framework, including joining data from other collections, using the, operator, and creating materialized views using. Financial trends: Making financial predictionsfor example, stock market predictionsis quite easy with a time series database, as it stores a lot of contextual data that can be cross-referenced later, for analysis. How to vertical center a TikZ node within a text line? This second method is one that MongoDB itself (and other blogs) recommends and therefore we call Mongo-recommended when it comes to time series. To learn more, see our tips on writing great answers. TimescaleDB outperforms both methods of storing time-series data in MongoDB, by between 69% (vs. Mongo-naive) and 160% (vs. Mongo-recommended). These functions are optimized for performance and help in faster decision-making. insert if it doesnt exist, otherwise update) pattern is not needed. Starting in MongoDB 5.0 there is a new collection type, time-series collections, which are specifically designed for storing and working with time-series data without the hassle or need to worry about low-level model optimization. You can select the range of documents you're interested in with a similar query to the one above, then pick out only the ones at the intervals you're interested in with the map function. As data is sent at regular intervals and writes are fast and consistent, data can be sent to a streaming engine to perform real-time analytics and visualization. Moreover, for the ~1 billion benchmark dataset, the Mongo-recommended method used more disk space than both the Mongo-naive method and TimescaleDB making it worse than Mongo-naive on insert performance. This is particularly useful to analyze IoT data, financial data, weather forecasting, and many other real-life use cases. In addition to the append only nature, in the initial release, time series collections will not work with Change Streams, Realm Sync, or Atlas Search. This resulted in MongoDB becoming a go to platform for developers in most domains and use-cases including eCommerce, telecom, financial services, healthcare. There are yet another class of products such as Prometheus, Splunk etc., that store time series log data for analytics. In particular, when aggregating one or more metrics on a single device for a single hour, the two databases show fairly equal performance. This is for the purposes of doing a quick comparison only. The four components of time series are based on different aspects of movement of time series: Trends: Trends show the increase and decrease over a period of timefor example, population, items in inventory, and number of schools opened. By this point, Mongo-naive had demonstrated better write performance with a simpler implementation and lower disk usage, but we suspected that Mongo-recommended would outperform Mongo-naive for query performance, justifying its recommendation by the MongoDB team and users. Having settled that, lets move on to comparing MongoDB and TimescaleDB. MongoDB time series collections are writable non-materialized views on internal collections that automatically organize time series data into an optimized storage format on insert. TSD contains many functions like aggregation, grouping, comparison, machine learning, and other similar functions to perform complex analysis on the data. import pymongo import time from datetime import datetime client = pymongo.MongoClient() db = client['time-series-db'] col = db['time-series-col'] # . This includes the basic CRUD (Create, Read, Update, and Delete) features, as well as features like high availability, scalability, and reliability. IoT, eCommerce, FinTech, healthcare, infrastructure monitoring and other industries that generate petabytes of time series data are only going to increase manifold. Finally we look at two types of queries where TimescaleDB outperforms MongoDB by an ever wider margin. Second, to unpack the 60x60 matrices in each document, the $unwind/$project/$unwind pattern is needed to efficiently expand those matrices while removing empty time periods. I am trying to get an idea of how snappy queries for particular time ranges will be once we have a lot of data. So for time-series data with TimescaleDB, you get all the benefits of a reliable relational database (i.e., PostgreSQL) with better performance than a popular NoSQL solution like MongoDB. You generally look for. When aggregating one metric per device, per hour, for some 24 hour window, TimescaleDB showed 1507% (or 15x) the performance of MongoDB. To call out a few of them, MongoDB 5.0 introduced native time series data. However these approaches come with limitations. Beyond millisecond precision is probably infeasible, as MongoDBs document size limitation means that you would probably need a to create a document per second for each device and the nesting required would make the query construction process extremely complex.
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