I'm new at DynamoDB technologies but not at NoSQL (I've already done some project using Firebase).
Read that a DynamoDB best practice is one table per application I've been having a hard time on how to design my 1 to N relationship.
I have this entity (pseudo-json):
{
machineId: 'HASH_ID'
machineConfig: /* a lot of fields */
}
A machineConfig is unique for each machine and can change rarely and only by an administration (no consistency issue here).
The issue is that I have to manage a log of data from the sensors of each machine. The log is described as:
{
machineId: 'HASH_ID',
sensorsData: [
/* Huge list of: */
{ timestamp: ..., data: /* lot of fields */ },
...
]
}
I want to keep my machineConfig in one place. Log list can't be insert into the machine entity because it's a continuous stream of data taken over time.
Furthermore, I don't understand which could be the composite key, the partition key obviously is the machineId, but what about the order key?
How to design this relationship taking into account the potential dimensions of data?
You could do this with 1 table. The primary key could be (machineId, sortKey) where machineId is the partition key and sortKey is a string attribute that is going to be used to cover the 2 cases. You could probably come up with a better name.
To store the machineConfig you would insert an item with primary key (machineId, "CONFIG"). The sortKey attribute would have the constant value CONFIG.
To store the sensorsData you could use the timestamp as the sortKey value. You would insert a new item for each piece of sensor data. You would store the timestamp as a string (as time since the epoch, ISO8601, etc)
Then to query everything about a machine you would run a Dynamo query specifying just the machineId partition key - this would return many items including the machineConfig and the sensor data.
To query just the machineConfig you would run a Dynamo query specifying the machineId partition key and the constant CONFIG as the sortKey value
To query the sensor data you could specify an exact timestamp or a timestamp range for the sortKey. If you need to query the sensor data by other values then this design might not work as well.
Editing to answer follow up question:
You would have to resort to a scan with a filter to return all machines with their machineId and machineConfig. If you end up inserting a lot of sensor data then this will be a very expensive operation to perform as Dynamo will look at every item in the table. If you need to do this you have a couple of options.
If there are not a lot of machines you could insert an item with a primary key like ("MACHINES", "ALL") and a list of all the machineIds. You would query on that key to get the list of machineIds, then you would do a bunch of queries (or a batch get) to retrieve all the related machineConfigs. However since the max Dynamo item size is 400KB you might not be able to fit them all.
If there are too many machines to fit in one item you could alter the above approach a bit and have ("MACHINES", $machineIdSubstring) as a primary key and store chunks of machineIds under each sort key. For example, all machineIds that start with 0 go in ("MACHINES", "0"). Then you would query by each primary key 0-9, build a list of all machineIds and query each machine as above.
Alternatively, you don't have to put everything in 1 table - it is just a guideline that fits a lot of use cases. If there are too many machines to fit in less than 400KB but there aren't tens of thousands and you aren't trying to query all of them all the time, you could have a separate table of machineId and machineConfig that you resort to scanning when necessary.
Related
If I have a table of installed equipment with make and model, where the make and model will be duplicated a lot, but with a variety of spellings, etc, how is the best way to avoid wasted space from data duplication?
CREATE TABLE equipment (
id integer NOT NULL,
make character varying(128),
model character varying(128),
lat double precision,
lon double precision,
created timestamp without time zone,
updated timestamp without time zone
);
This table has a lot more fields in reality and will have many millions of rows, and I have other tables in a similar situation totalling about 600 GB of data.
The source data needs to be kept the same (ie. "Panasonic" and "PANASONIC" can't be combined / corrected), and the scale and variety of the data makes that impractical anyway.
I'm envisioning a separate key:value table that stores the values and then the ID is just stored in the equipment table, with a function where I just pass the value and it returns the ID (whether it looks it up and returns the ID or inserts it and returns the new ID).
That would make the tables become:
CREATE TABLE equipment (
id integer NOT NULL,
make integer,
model integer,
lat double precision,
lon double precision,
created timestamp without time zone,
updated timestamp without time zone
);
CREATE TABLE lookup (
id integer NOT NULL,
value character varying(128),
updated timestamp without time zone
);
And interacting with the table would be:
SELECT
id,
lookup_value(make) AS make,
lookup_value(model) AS model,
lat,
lon,
created,
updated
FROM
equipment
INSERT INTO
equipment (id, make, model, created)
VALUES
(nextval('equipment_id_seq'::regclass), lookup_value('Panasonic'), lookup_value('ABC123-G'), NOW())
The lookup table could be reused among a variety of fields and tables, with each string value only appearing once, and the key:value staying the same forever (changing from "Panasonic" and "PANASONIC" wouldn't change the key for "Panasonic", it would return the key for "PANASONIC" instead, inserting if needed).
What are the problems with this approach (aside from code complexity)?
Is there a better approach?
You would never want to have generic lookup table like this. For one it means you cant create a foreign key between the two "value" columns and the IDs, because there is no way of stopping an entry for Make ending up in Model.
As #a_horse_with_no_name said, you would be better to create a model and make table, with FK between them, and then do as you say where you only save a new model or make if it doesn't already exist.
I would also be tempted to have a third column so for all the possible spellings for PANASONIC for example, you have both the lookup row for what they entered, and a reference to what they probably meant. That would assist in cleaning up data going forward. You could suggest in the UI "Did you mean Panasonic" when they enter "Panasoonic" for example.
Coding us up to you, either in a single update, stored proc, or app code.
Goal is to make querying as fast as possible.
Postgres table contains 10.000.000 records, each record has 30 various properties.
CREATE TABLE films (
code char(5) CONSTRAINT firstkey PRIMARY KEY,
title varchar(40) NOT NULL,
did integer NOT NULL,
date_prod date,
kind varchar(10),
len interval hour to minute
-- and ~25 more columns
);
Users are filtering data in very specific ways, but always based on bunch of conditions.
For example, user A needs to paginate through these 10mm records filtered by column code, title, did and date_prod and ordered by column date_prod and title. And he is performing just few more similar combinations, but he repeats search many times a day. So, main point is: conditions are complex, but variety of combinations is small. Usually just 3-5 per user.
May be this is also important: user wants to see only some columns, not all of them. And this is related to conditions he uses in query.
Records in this table is being updated many times a day and therefore each time user will see updated data, so caching will not work here.
This app is in use by small quantity of users (less than 10.000) and will never exceed this number.
What i need here is to make queries as fast as it's possible. It's okay, if each first time when user creates new search (bunch of query conditions + very specific set of columns) it will be taking seconds to give the results. But if this user saves this set of conditions and columns, i need to make all further repetitions of this search as fast as possible despite the fact that the data is being updated all the time.
I doubt that indexing each column is a good idea. So, how do i do that? PostgreSQL with material views? May be MongoDB or other nosql solution somehow will work better here?
I've just started looking into Amazon's DynamoDB. Obviously the scalability appeals, but I'm trying to get my head out of SQL mode and into no-sql mode. Can this be done (with all the scalability advantages of dynamodb):
Have a load of entries (say 5 - 10 million) indexed by some number. One of the fields in each entry will be a creation date. Is there an effective way for dynamo db to give my web app all the entries created between two dates?
A more simple question - can dynamo db give me all entries in which a field matches a certain number. That is, there'll be another field that is a number, for argument's sake lets say between 0 and 10. Can I ask dynamodb to give me all the entries which have value e.g. 6?
Do both of these queries need a scan of the entire dataset (which I assume is a problem given the dataset size?)
many thanks
Is there an effective way for dynamo db to give my web app all the
entries created between two dates?
Yup, please have a look at the of the Primary Key concept within Amazon DynamoDB Data Model, specifically the Hash and Range Type Primary Key:
In this case, the primary key is made of two attributes. The first
attributes is the hash attribute and the second one is the range
attribute. Amazon DynamoDB builds an unordered hash index on the hash
primary key attribute and a sorted range index on the range primary
key attribute. [...]
The listed samples feature your use case exactly, namely the Reply ( Id, ReplyDateTime, ... ) table facilitates a primary key of type Hash and Range with a hash attribute Id and a range attribute ReplyDateTime.
You'll use this via the Query API, see RangeKeyCondition for details and Querying Tables in Amazon DynamoDB for respective examples.
can dynamo db give me all entries in which a field matches a certain
number. [...] Can I ask dynamodb to give
me all the entries which have value e.g. 6?
This is possible as well, albeit by means of the Scan API only (i.e. requires to read every item in the table indeed), see ScanFilter for details and Scanning Tables in Amazon DynamoDB for respective examples.
Do both of these queries need a scan of the entire dataset (which I
assume is a problem given the dataset size?)
As mentioned the first approach works with a Query while the second requires a Scan, and Generally, a query operation is more efficient than a scan operation - this is a good advise to get started, though the details are more complex and depend on your use case, see section Scan and Query Performance within the Query and Scan in Amazon DynamoDB overview:
For quicker response times, design your tables in a way that can use
the Query, Get, or BatchGetItem APIs, instead. Or, design your
application to use scan operations in a way that minimizes the impact
on your table's request rate. For more information, see Provisioned Throughput Guidelines in Amazon DynamoDB.
So, as usual when applying NoSQL solutions, you might need to adjust your architecture to accommodate these constraints.
I'm fairly new to this so you may have to bear with me. I'm developing a database for a website with athletics rankings on them and I was curious as to how many tables would be the most efficient way of achieving this.
I currently have 2 tables, a table called 'athletes' which holds the details of all my runners (potentially around 600 people/records) which contains the following fields:
mid (member id - primary key)
firstname
lastname
gender
birthday
nationality
And a second table, 'results', which holds all of their performances and has the following fields:
mid
eid (event id - primary key)
eventdate
eventcategory (road, track, field etc)
eventdescription (100m, 200m, 400m etc)
hours
minutes
seconds
distance
points
location
The second table has around 2000 records in it already and potentially this will quadruple over time, mainly because there are around 30 track events, 10 field, 10 road, cross country, relays, multi-events etc and if there are 600 athletes in my first table, that equates to a large amount of records in my second table.
So what I was wondering is would it be cleaner/more efficient to have multiple tables to separate track, field, cross country etc?
I want to use the database to order peoples results based on their performance. If you would like to understand better what I am trying to emulate, take a look at this website http://thepowerof10.info
Changing the schema won't change the number of results. Even if you split the venue into a separate table, you'll still have one result per participant at each event.
The potential benefit of having a separate venue table would be better normalization. A runner can have many results, and a given venue can have many results on a given date. You won't have to repeat the venue information in every result record.
You'll want to pay attention to indexes. Every table must have a primary key. Add additional indexes for columns you use in WHERE clauses when you select.
Here's a discussion about normalization and what it can mean for you.
PS - Thousands of records won't be an issue. Large databases are on the order of giga- or tera-bytes.
My thought --
Don't break your events table into separate tables for each type (track, field, etc.). You'll have a much easier time querying the data back out if it's all there in the same table.
Otherwise, your two tables look fine -- it's a good start.
I have a database that stores some users in it. Each user has its account settings, privacy settings and lots of other properties to set. The number of those properties started to grow and I could end up with 30 properties or so.
Till now, I used to keep it in "UserInfo" table having User and UserInfo related as One-To-Many (keeping a log of all changes). Putting it in a single "UserInfo" table doesn't sound nice and, at least in the database model, it would look messy. What's the solution?
Separating privacy settings, account settings and other "groups" of settings in separate tables and have 1-1 relations between UserInfo and each group of settings table is one solution, but would that be too slow (or much slower) when retrieving the data? I guess all data would not be presented on a single page at the same moment. So maybe having one-to-many relationships to each table is a solution too (keeping log of each group separately)?
If it's only 30 properties, I'd recommend just creating 30 columns. That's not too much for a modern database to handle.
But I would guess that if you ahve 30 properties today, you will continue to invent new properties as time goes on, and the number of columns will keep growing. Restructuring your table to add columns every day may become time-consuming as you get lots of rows.
For an alternative solution check out this blog for a nifty solution for storing lots of dynamic attributes in a "schemaless" way: How FriendFeed Uses MySQL.
Basically, collect all the properties into some format and store it in a single TEXT column. The format is semi-structured, that is your application can separate the properties if needed but you can also add more at any time, or even have different properties per row. XML or YAML or JSON are example formats, or some object serialization format supported by your application code language.
CREATE TABLE Users (
user_id SERIAL PRIMARY KEY,
user_proerties TEXT
);
This makes it hard to search for a given value in a given property. So in addition to the TEXT column, create an auxiliary table for each property you want to be searchable, with two columns: values of the given property, and a foreign key back to the main table where that particular value is found. Now you have can index the column so lookups are quick.
CREATE TABLE UserBirthdate (
user_id BIGINT UNSIGNED PRIMARY KEY,
birthdate DATE NOT NULL,
FOREIGN KEY (user_id) REFERENCES Users(user_id),
KEY (birthdate)
);
SELECT u.* FROM Users AS u INNER JOIN UserBirthdate b USING (user_id)
WHERE b.birthdate = '2001-01-01';
This means as you insert or update a row in Users, you also need to insert or update into each of your auxiliary tables, to keep it in sync with your data. This could grow into a complex chore as you add more auxiliary tables.