I have users, each user gets assigned 12 events(they can reschedule these events etc) every 2 months. Each event is an object with id, name, description, date, is completed.
I'm currently saving these events in the user's document so that I do only one document read. events:[{events}*12] after a year there will be 72 events in this array, and it would keep growing year after year.
I'm wondering, should I be concerned with the 1mb limit?
I'd like to preserve history, so that the user can also view events of the past.
Given that on the calendar at most you could see one months worth of events, and say I lazy loaded the previous month for speed, doing a subcollection for events would result to 12-24 document reads. I fear this would get expensive very quick.
Any advice would be appreciated, thanks.
Honestly, I wouldn't be too concerned with the 1MB limit, that is still a lot of characters (roughly 1 million, although may be a bit less depending on data types) - so unless the descriptions could be incredibly long I think it's unlikely you will reach anywhere near those limits.
That being said, if it is a concern you could schedule a cloud function to periodically (perhaps every 3 months) to archive or move events to a subcollection that are no longer still of use, storing across more documents (to represent the quarter, or year, or whatever time period you decide on)
Related
My application is going to be creating a growing number of materialized views.
The number of materialized views will increase by 8,000 - 10,000 per month.
Most of the views will hold very little information, around 100-1000 rows with small fields, but a few (10 per month) will hold from 100,000 to millions of rows with small fields.
I am cautious to see if this is a good idea before going too far into the implementation.
Can anyone tell me the hard limits I may hit, or if this is a good idea at all?
If needed, I can explain further the use-case. It may be possible to drop some of the older views if needed (99%+), but not all (the 10/month big ones must stay).
EDIT: Explanation Requested
The app allows users to vote on content, and then we make charts of the content with the most votes. We have 5-minute units of voting, and charts generated by request. A user can look at any length of time with a granularity of 5 minutes. For example, I could look at just the past 5 minutes of votes, 15 minutes, 2 hours, etc.
To tally votes and make the "Top Charts" we must do an expensive query, searching all the content that has gotten votes within the time units that are requested, tallying, and sorting. To mitigate this, I wanted to make a sorted materialized view with the results of the vote for EVERY time unit, as after voting closes for that 5 minutes, the votes will never change. This way, a popular search (like the latest 5-minute chart) will not have to be generated and sorted every time a user searches (there are 8760 5-minute units per month). I also wanted to make mviews for the weeks, months, and days.
This is the table I will be using to generate the mviews, with tuid being a reference to the 5-minute voting time unit
Perhaps there is a better way to make this efficient with caching?
I'm maintaining a system where users create something called "books" that are accessed by other users.
I need a convenient (good performance) way to store events in database where users visit these books to later display graphs with statistics. The graphs need to demonstrate a history where the owner of the book can see which days in the week, and at which times there is more visiting activity (all over the months).
Using ERD (Entity-Relationship-Diagram), I can produce the following Conceptual Model:
At first the problem seems to be solved, as we have a very simple situation here. This will give me a table with 3 fields. One will be the occurrence of the visit event, and the other 2 will be foreign keys. One represents the user, while the other represents which book was visited. In short, every record in this table will be a visit:
However, thinking that a user can average about 10 to 30 book visits per day, and having a system with 100.000 users, in a single day this table can add many gigabytes of new records. I'm not the most experienced person in good database performance practices, but I'm pretty sure that this is not the solution.
Even though I do a cleanup on the database to delete old records, I need to keep a record history of the last 2 months of visits (at least).
I've been looking for a way to solve this for days, and I have not found anything yet. Could someone help me, please?
Thank you.
OBS: I'm using PostgreSQL 9.X, and the system is written in Java.
As mentioned in the comments, you might be overestimating data size. Let's do the math. 100k users at 30 books/day at, say, 30 bytes per record.
(100_000 * 30 * 30) / 1_000_000 # => 90 megabytes per day
Even if you add index size and some amount of overhead, this is still a few orders of magnitude lower than "many gigabytes per day".
We are in the process of evaluating time series databases (TSDB) for our project.
My use case is to store historical events emanating from various sensors. The events can contain one or more attributes of different data types(e.g., strings, float, int etc).
As part of this evaluation exercise we came across few online materials where people say that certain type of TSDBs are suitable for metric stores, certain types are suitable for ,event stores and certain others are for both. Am a bit confused about the differences between metrics and events. Aren't metrics some kind of events? Can someone please help in understanding the difference in this context?
Metrics and events are two different types of time series data: regular and irregular, respectively. Regular data (metrics) are evenly distributed across time and can be used for processes like forecasting. Irregular data (events) are unpredictable, and while they still occur in temporal order, the intervals between events are inconsistent, which means that using them for forecasting or averaging could lead to unreliable results.
The basic difference is metrics occur at regular intervals and events don’t. Imagine I’m monitoring my personal website — I want to track the response codes to make sure the site is available, so I collect them at frequent intervals. I could then query those response code metrics to figure out what percentage of the time my site was down (because it was too popular). But I also want to know when a user clicks on an ad. I don’t know when or if this click will happen, so collecting at a regular interval doesn’t make sense. If I have 12 clicks for the past year, the average will be one click a month regardless if they could have all happened October (the peak of my popularity).
Assume an app that collects real-time temperature data for various cities around the world every 10 minutes.
Using the following GAE datastore model,
class City(db.Model):
name = db.StringProperty()
class DailyTempData(db.Model):
date = db.DateProperty()
temp_readings = db.ListProperty(float, indexed=False) # appended every 10 minutes
and a cron.yaml as so,
cron:
- description: read temperature
url: /cron/read_temps
schedule: every 10 minutes
I am already hitting GAE's daily free quota for datastore writes, and I'm looking for ways to get around this problem.
I'm thinking of reducing my datastore writes by persisting the temperature data only at the end of each day, which will effectively reduce the daily write volume (for each city) from 144 times to 1.
One way to do this is to use memcache as a temporary scratchpad, but due to the possibility of random data evictions, I could well lose all my data for the day. (Aside question: from experience, how often does unplanned eviction really happen?)
Questions are as follows:
Is there such a memory/storage facility (persistent and guaranteed across cron jobs) that would allow me to reduce datastore writes as described?
If not, what could be some alternative solutions?
The only other requirement would be that the temperature readings must be accessible (for serving to client-side) any given time of day.
The only guaranteed storage in the datastore.
As to memcache evictions - it's depends on what is going on, in your app and in google appengine land, evictions could be within a minute or two or after hours. In my appengine instances I usually have oldest items sitting at about 2 hours old. But it all depends and you just can't rely on it.
tasks queues payload is about 10K.
You could just write a blob (containing all cities measured in the 10min interval) and then reprocess it and unpick it and write out the city details at the end of the day.
When you say clients must be able to access temperature readings, do you mean just the current or all the readings for the day.
You could also change your model, so that a huge object is stored for each execution or the cron. Not just for each city, I mean.
For example, say the object is called Measures... A Measures item will contain a List of all your measures for the corresponding time. Store them as non-indexed properties and you should have no problems... And also just 144 writes a day.
For the reading part... Use memcache to store the Measures items, as a good usage pattern.
I am working with google app engine and using the low leval java api to access Big Table. I'm building a SAAS application with 4 layers:
Client web browser
RESTful resources layer
Business layer
Data access layer
I'm building an application to help manage my mobile auto detailing company (and others like it). I have to represent these four separate concepts, but am unsure if my current plan is a good one:
Appointments
Line Items
Invoices
Payments
Appointment: An "Appointment" is a place and time where employees are expected to be in order to deliver a service.
Line Item: A "Line Item" is a service, fee or discount and its associated information. An example of line items that might go into an appointment:
Name: Price: Commission: Time estimate
Full Detail, Regular Size: 160 75 3.5 hours
$10 Off Full Detail Coupon: -10 0 0 hours
Premium Detail: 220 110 4.5 hours
Derived totals(not a line item): $370 $185 8.0 hours
Invoice: An "Invoice" is a record of one or more line items that a customer has committed to pay for.
Payment: A "Payment" is a record of what payments have come in.
In a previous implementation of this application, life was simpler and I treated all four of these concepts as one table in a SQL database: "Appointment." One "Appointment" could have multiple line items, multiple payments, and one invoice. The invoice was just an e-mail or print out that was produced from the line items and customer record.
9 out of 10 times, this worked fine. When one customer made one appointment for one or a few vehicles and paid for it themselves, all was grand. But this system didn't work under a lot of conditions. For example:
When one customer made one appointment, but the appointment got rained out halfway through resulting in the detailer had to come back the next day, I needed two appointments, but only one line item, one invoice and one payment.
When a group of customers at an office all decided to have their cars done the same day in order to get a discount, I needed one appointment, but multiple invoices and multiple payments.
When one customer paid for two appointments with one check, I needed two appointments, but only one invoice and one payment.
I was able to handle all of these outliers by fudging things a little. For example, if a detailer had to come back the next day, i'd just make another appointment on the second day with a line item that said "Finish Up" and the cost would be $0. Or if I had one customer pay for two appointments with one check, I'd put split payment records in each appointment. The problem with this is that it creates a huge opportunity for data in-congruency. Data in-congruency can be a serious problem especially for cases involving financial information such as the third exmaple where the customer paid for two appointments with one check. Payments must be matched up directly with goods and services rendered in order to properly keep track of accounts receivable.
Proposed structure:
Below, is a normalized structure for organizing and storing this data. Perhaps because of my inexperience, I place a lot of emphasis on data normalization because it seems like a great way to avoid data incongruity errors. With this structure, changes to the data can be done with one operation without having to worry about updating other tables. Reads, however, can require multiple reads coupled with in-memory organization of data. I figure later on, if there are performance issues, I can add some denormalized fields to "Appointment" for faster querying while keeping the "safe" normalized structure intact. Denormalization could potentially slow down writes, but I was thinking that I might be able to make asynchronous calls to other resources or add to the task que so that the client does not have to wait for the extra writes that update the denormalized portions of the data.
Tables:
Appointment
start_time
etc...
Invoice
due_date
etc...
Payment
invoice_Key_List
amount_paid
etc...
Line_Item
appointment_Key_List
invoice_Key
name
price
etc...
The following is the series of queries and operations required to tie all four entities (tables) together for a given list of appointments. This would include information on what services were scheduled for each appointment, the total cost of each appointment and weather or not payment as been received for each appointment. This would be a common query when loading the calendar for appointment scheduling or for a manager to get an overall view of operations.
QUERY for the list of "Appointments" who's "start_time" field lies between the given range.
Add each key from the returned appointments into a List.
QUERY for all "Line_Items" who's appointment_key_List field includes any of the returns appointments
Add each invoice_key from all of the line items into a Set collection.
QUERY for all "Invoices" in the invoice ket set (this can be done in one asynchronous operation using app engine)
Add each key from the returned invoices into a List
QUERY for all "Payments" who's invoice_key_list field contains a key matching any of the returned invoices
Reorganize in memory so that each appointment reflects the line_items that are scheduled for it, the total price, total estimated time, and weather or not it has been paid for.
...As you can see, this operation requires 4 datastore queries as well as some in-memory organization (hopefully the in-memory will be pretty fast)
Can anyone comment on this design? This is the best I could come up with, but I suspect there might be better options or completely different designs that I'm not thinking of that might work better in general or specifically under GAE's (google app engine) strengths, weaknesses, and capabilities.
Thanks!
Usage clarification
Most applications are more read-intensive, some are more write intensive. Below, I describe a typical use-case and break down operations that the user would want to perform:
Manager gets a call from a customer:
Read - Manager loads the calendar and looks for a time that is available
Write - Manager queries customer for their information, I pictured this to be a succession of asynchronous reads as the manager enters each piece of information such as phone number, name, e-mail, address, etc... Or if necessary, perhaps one write at the end after the client application has gathered all of the information and it is then submitted.
Write - Manager takes down customer's credit card info and adds it to their record as a separate operation
Write - Manager charges credit card and verifies that the payment went through
Manager makes an outgoing phone call:
Read Manager loads the calendar
Read Manager loads the appointment for the customer he wants to call
Write Manager clicks "Call" button, a call is initiated and a new CallReacord entity is written
Read Call server responds to call request and reads CallRecord to find out how to handle the call
Write Call server writes updated information to the CallRecord
Write when call is closed, call server makes another request to the server to update the CallRecord resource (note: this request is not time-critical)
Accepted answer::
Both of the top two answers were very thoughtful and appreciated. I accepted the one with few votes in order to imperfectly equalize their exposure as much as possible.
You specified two specific "views" your website needs to provide:
Scheduling an appointment. Your current scheme should work just fine for this - you'll just need to do the first query you mentioned.
Overall view of operations. I'm not really sure what this entails, but if you need to do the string of four queries you mentioned above to get this, then your design could use some improvement. Details below.
Four datastore queries in and of itself isn't necessarily overboard. The problem in your case is that two of the queries are expensive and probably even impossible. I'll go through each query:
Getting a list of appointments - no problem. This query will be able to scan an index to efficiently retrieve the appointments in the date range you specify.
Get all line items for each of appointment from #1 - this is a problem. This query requires that you do an IN query. IN queries are transformed into N sub-queries behind the scenes - so you'll end up with one query per appointment key from #1! These will be executed in parallel so that isn't so bad. The main problem is that IN queries are limited to only a small list of values (up to just 30 values). If you have more than 30 appointment keys returned by #1 then this query will fail to execute!
Get all invoices referenced by line items - no problem. You are correct that this query is cheap because you can simply fetch all of the relevant invoices directly by key. (Note: this query is still synchronous - I don't think asynchronous was the word you were looking for).
Get all payments for all invoices returned by #3 - this is a problem. Like #2, this query will be an IN query and will fail if #3 returns even a moderate number of invoices which you need to fetch payments for.
If the number of items returned by #1 and #3 are small enough, then GAE will almost certainly be able to do this within the allowed limits. And that should be good enough for your personal needs - it sounds like you mostly need it to work, and don't need to it to scale to huge numbers of users (it won't).
Suggestions for improvement:
Denormalization! Try storing the keys for Line_Item, Invoice, and Payment entities relevant to a given appointment in lists on the appointment itself. Then you can eliminate your IN queries. Make sure these new ListProperty are not indexed to avoid problems with exploding indices
Other less specific ideas for improvement:
Depending on what your "overall view of operations" is going to show, you might be able to split up the retrieval of all this information. For example, perhaps you start by showing a list of appointments, and then when the manager wants more information about a particular appointment you go ahead and fetch the information relevant to that appointment. You could even do this via AJAX if you this interaction to take place on a single page.
Memcache is your friend - use it to cache the results of datastore queries (or even higher level results) so that you don't have to recompute it from scratch on every access.
As you've noticed, this design doesn't scale. It requires 4 (!!!) DB queries to render the page. That's 3 too many :)
The prevailing notion of working with the App Engine Datastore is that you want to do as much work as you possibly can when something is written, so that almost nothing needs to be done when something is retrieved and rendered. You presumably write the data very few times, compared to how many times it's rendered.
Normalization is similarly something that you seem to be striving for. The Datastore doesn't place any value in normalization -- it may mean less data incongruity, but it also means reading data is muuuuuch slower (4 reads?!!). Since your data is read much more often than it's written, optimize for reads, even if that means your data will occasionally be duplicated or out of sync for a short amount of time.
Instead of thinking about how the data looks when it's stored, think about how you want the data to look when it's displayed to the user. Store as close to that format as you can, even if that means literally storing pre-rendered HTML in the datastore. Reads will be lightning-fast, and that's a good thing.
So since you should optimize for reads, oftentimes your writes will grow to gigantic proportions. So gigantic that you can't fit it in the 30 second time limit for requests. Well, that's what the task queue is for. Store what you consider the "bare necessities" of your model in the datastore, then fire off a task queue to pull it back out, generate the HTML to be rendered, and put it in there in the background. This might mean your model is immediately ready to display until the task has finished with it, so you'll need a graceful degradation in this case, even if that means rendering it "the slow way" until the data is fully populated. Any further reads will be lightning-quick.
In summary, I don't have any specific advice directly related to your database -- that's dependent on what you want the data to look like when the user sees it.
What I can give you are some links to some super helpful videos about the datastore:
Brett Slatkin's 2008 and 2009 talks on building scalable, complex apps on App Engine, and a great one from this year about data pipelines (which isn't directly applicable I think, but really useful in general)
App Engine Under the Covers: How App Engine does what it does, behind the scenes
AppStats: a great way to see how many datastore reads you're performing, and some tips on reducing that number
Here are a few app-engine specific factors that I think you'll have to contend with:
When querying using an inequality, you can only use an inequality on one property. for example, if you are filtering on an appt date being between July 1st and July 4th, you couldn't also filter by price > 200
Transactions on app engine are a bit tricky compared to the SQL database you are probably used to. You can only do transactions on entities that are in the same "entity group".