I have a table like:
CREATE TABLE videos_by_tags (
tag text,
video_id uuid,
...
PRIMARY KEY ((tag) video_id)
)
which will support queries such as get videos by tagA. If in the future I need to support queries like get videos by tagA and tabB and tagC, how would I set up my cassandra tables?
Is there a specific way to set up tables for this scenario, or do I have to query in a specific way?
Or would I have to resort to grabbing an x of amount of results for each tag criteria (ie: 100 items from tagA, 100 items from tagB, etc) and manually determine the same, unique items from each set?
Related
I have a very simple data table. But after reading a lot of examples in the internet, I am still more and more confused how to solve the following scenario:
1) The Table
My data table looks like this (without defining the primayr key, as this is my understanding problem):
CREATE TABLE documents (
uid text,
created text,
data text
}
Now my goal is to have to different ways to select data.
2) Select by the UID:
SELECT * FROM documents
WHERE uid = ‘xxxx-yyyyy-zzzz’
3) Select by a date limit
SELECT * FROM documents
WHERE created >= ‘2015-06-05’
So my question is:
What should my table definition in Cassandra look like, so that I can perform these selections?
To achieve both queries, you would need two tables.
First one would look like:
CREATE TABLE documents (
uid text,
created text,
data text,
PRIMARY KEY (uid));
and you retrieve your data with: SELECT * FROM documents WHERE uid='xxxx-yyyy-zzzzz' Of course, uid must be unique. You might want to consider the uuid data type (instead of text)
Second one is more delicate. If you set your partition to the full date, you won't be able to do a range query, as range query is only available on the clustering column. So you need to find the sweet spot for your partition key in order to:
make sure a single partition won't be too large (max 100MB,
otherwise you will run into trouble)
satisfy your query requirements.
As an example:
CREATE TABLE documents_by_date (
year int,
month int,
day int,
uid text,
data text,
PRIMARY KEY ((year, month), day, uid);
This works fine if within a day, you don't have too many documents (so your partition don't grow too much). And this allows you to create queries such as: SELECT * FROM documents_by_date WHERE year=2018 and month=12 and day>=6 and day<=24; If you need to issue a range query across multiple months, you will need to issue multiple queries.
If your partition is too large due to the data field, you will need to remove it from documents_by_date. And use documents table to retrieve the data, given the uid you retreived from documents_by_date.
If your partition is still too large, you will need to add hour in the partition key of documents_by_date.
So overall, it's not a straightforward request, and you will need to find the right balance for yourself when defining your partition key.
If latency is not a huge concern, an alternative would be to use the stratio lucene cassandra plugin, and index your date.
Question does not specify how your data is going to be with respect user and create time. But since its a document, I am assuming that one user will be creating one document at one "created" time.
Below is the table definition you can use.
CREATE TABLE documents (
uid text,
created text,
data text
PRIMARY KEY (uid, created)
) WITH CLUSTERING ORDER BY (created DESC);
WITH CLUSTERING ORDER BY (created DESC) can help you get the data order by created for a given user.
For your first requirement you can query like given below.
SELECT * FROM documents WHERE uid = 'SEARCH_UID';
For your second requirement you can query like given below
SELECT * FROM documents WHERE created > '2018-04-10 11:32:00' ALLOW FILTERING;
Use of Allow Filtering should be used diligently as it scans all partitions. If we have to create a separate table with date as primary key, it becomes tricky if there are many documents being inserted at very same second. Clustering order works best for the requirements where documents for a given user need to be sorted by time.
Suppose I have an items table in Cassandra like this:
CREATE TABLE items (
id uuid,
tags set<text>,
name text,
available boolean,
PRIMARY KEY (id));
So I have basically items with tags, the number of tags is unknown and can reach up to few hundreds and different items may share some tags. I have two requirements:
I want to query the number of items which contain a specific set
of tags, for example I want to know the number of items with tag1 or
tag2, or tag200 in their tags column.
I want to update all items
which contain a specific set of tags (found in requirement 1), so for
example I want to set available = true for all items with tag1 or
tag2, or tag200 in their tags column.
These can be done using Spark, or a secondary index, but I don't want to use that and would like to explore if these can be done using pure CQL effectively. So maybe I have to start with a table like this:
CREATE TABLE itemsByTag (
id uuid,
tag text,
tags set<text>,
name text,
available boolean,
PRIMARY KEY ((tag),id);
But this table can give me the same item twice or more (say that item1 has tag1 and tag2 and tag200 then I will get item1 three times when I query the previous table by tag) Also, how to construct a suitable table for a Cassandra counter table to satisfy requirement 1?
in RDBMS this is done using three tables as mentioned here, the question is how to denormalize this effectivly in Cassandra.
I am new to cassandra and am coming from Postgres. I was wondering if there is a way that I can get data from 2 different tables or column family and then return the results. I have this query
select p.fullname,p.picture s.post, s.id, s.comments, s.state, s.city FROM profiles as p INNER JOIN Chats as s ON(p.id==s.profile_id) WHERE s.latitudes>=28 AND 29>= s.latitudes AND s.longitudes
">=-21 AND -23>= s.longitudes
The query has 2 tables: Profiles and Chat and they both share a common field Chats.id==Proifles.profile_id it boils down to this basically return all rows where Chat ID is equal to Profiles id. I would like to keep it that way because now updating profiles are simple and would only need to update 1 row per profile update instead of de-normalizing everything and updating thousands of records. Any help or suggestions would be great
You have to design tables in way you won't need joins. Best practice is if your table matches exactly the use case it is used for.
Cassadra has a feature called shared static columns; this allows you to bind values with partition part of primary key. Thus, you can create "joined" version of table without duplicates.
CREATE TABLE t (
p_id uuid,
p_fullname text STATIC,
p_picture text STATIC,
s_id uuid,
s_post text,
s_comments text,
s_state text,
s_city text,
PRIMARY KEY (p_id, s_id)
);
I have a massively huge table with hundreds of billions of records and I mean to add a field in this table of which the same value would be repeated for millions of records. I don't know how to efficiently model this in cassandra. Allow me to elaborate:
I have a generic table:
CREATE TABLE readings (
key int,
key2 int,
time timestamp,
name text,
PRIMARY KEY ((key, key2) time)
)
This table has 700.000.000+ records.
I want to create a field in this table, named source. This field indicates where the record was gotten from (since the software has many ways of receiving the information on the reading table). One possible value for this field is "XML: path\to\file.xml" or "Direct import from the X database" or even "Manually added", I want this to be a descriptive field, used exclusively to allow later maintenance in the database where we want to manipulate only records from a given source.
The queries I want to run that I can't now are:
Which records on the readings table were gotten from a given source?
What is the source of a given record?
A solution would be for me to create a table such as:
CREATE TABLE readings_per_source(
source text,
key int,
key2 int,
time timestamp,
PRIMARY KEY (source, key, key2, time)
)
which would allow me to execute the first query, but would also mean that I would create 700.000.000+ new records on my database with a lot of information, which would take a lot of unnecessary storage space since tens of millions of these records would have the same value for source.
If this was a relational environment, I would create a source_id field on the readings table and a source table with id (PK) and name fields, that would mean storing only an additional integer for each row on the readings table and a new table with as many records as different sources there was.
How does one go about modelling this in cassandra?
Your schema
CREATE TABLE readings_per_source(
source text,
key int,
key2 int,
time timestamp,
PRIMARY KEY (source, key, key2, time)
)
is a very bad idea because source is the partition key and you can have millions of records sharing the same source e.g. having a very very wide partition --> hot spots
For you second query, What is the source of a given record? is it quite trivial if you access the data using the record primary keys (key, key2). The source column can be added as just a regular column into the table
For the first query Which records on the readings table were gotten from a given source? it is trickier. The idea here is to fetch all the records having the same source.
Do you realize that this query can potentially return tens of millions of records ?
If it's what you want to do, there is a solution, use the new SASI secondary index (read my blog post for all details) and create an index on the source column
CREATE TABLE readings (
key int,
key2 int,
time timestamp,
name text,
source text,
PRIMARY KEY ((key, key2), time)
)
CREATE CUSTOM INDEX source_idx ON readings(source)
USING 'org.apache.cassandra.index.sasi.SASIIndex'
WITH OPTIONS = {
'mode': 'PREFIX',
'analyzer_class': 'org.apache.cassandra.index.sasi.analyzer.NonTokenizingAnalyzer',
'case_sensitive': 'false'
};
Then to fetch all records having the same source, use server-side paging feature of the Java driver (or any other Datastax driver)
http://www.datastax.com/2015/03/how-to-do-joins-in-apache-cassandra-and-datastax-enterprise is a pretty good article on how to go about joining tables in Cassandra.
normalized data will always take up less storage than de-normalized (flat) data (provided the related data is larger than the key being used to join the tables together) but requires joins which take more horsepower to compute during queries.
There's always a trade-off. There's also a tradeoff concerning state with fully normalized data, one example being the customer who changes addresses. In a fully normalized schema, once the address change is made, all invoices for the customer, past and present show the new address. This isn't always desirable.
Often it's desirable to partially normalize to provide historic state on records where it's important to show the state of the data at a given time, such as on invoices. In that case you'd store a copy of the customer address data on the invoice at the time of invoice creation.
This is especially important for pricing and taxes as well. You want the price/tax stored with the invoice so you can show what the customer paid at the time the invoice was created, so when accounting runs monthly, yearly and beyond numbers that the prices on a given invoice are correct for the date on the invoice, even though the prices of the products may have changed. Otherwise you have an accounting nightmare!
There is a lot more to consider than simply storage space when deciding how to normalize/de-normalize a schema.
Sorry for rambling...
I'm having trouble designing a column family that suits the following requirement:
I would like to update X rows that match some condition for a field that is not the primary key and is not unique.
For example if a User column family has ID, name and birthday columns, I would like to update all the users that were born after some specific day.
Even if I add the 'birthday' to the primary key (lets say 'ID', 'birthday') I cannot perform this query because part of the primary key is missing.
How can i approach this by designing my column family differently ?
Thanks.
According to cassandra docs, there is no way to update rows without explicitly defining their partition key. This was done not by an accident, but because this feature (e.g. update users set status=1 where id>10) can allow user to update all data in table at once, which can be very-very-very expensive on large databases. Cassandra explicitly forbids all operations requiring data scans within multiple partitions.
To update multiple users all at once, you have to know their IDs. Having a table defined as:
CREATE TABLE stackoverflow.users (
id timeuuid PRIMARY KEY,
dob timestamp,
status text
)
and knowing user's primary key, you can run queries like update users set status='foo' where id in (1,2,3,4). But queries with really large sets of keys inside IN statement may cause performance issues on C*.
But how can you have an efficient range query like select id from some_table where dob>'2000-01-01 00:00:01'? There are two options available, and both of them are not really acceptable:
Create an index table like
CREATE TABLE stackoverflow.dob_index (
year int,
dob timestamp,
ids list<timeuuid>,
PRIMARY KEY (year, dob)
)
with compound partition+clustering primary key and use multiple queries like select * from dob_index where year=2014 and dob<'2014-05-01 00:00:01'; to fetch ids for different years. Notice that I've defined multiple partitions for the table to have some kind of even partition distribution in cluster. But the general idea is that you really shouldn't have a small amount of very large partitions. Prefer a large amount of small ones, if there's a choice.
Have a separate stand-alone index available for complex queries (like ElasticSearch/Solr/Sphinx).
But I suggest you to revisit your application logic in a way to avoid updating/deleting data at all:
instead of updating users table directly, you can have a separate table user_status you insert new statuses:
CREATE TABLE user_statuses (
id timeuuid,
updated_at timestamp,
status text,
PRIMARY KEY (id, updated_at)
)
When you need to scan/update a lot of rows at once, prefer using tools like Spark to efficiently distribute your workload among your cluster nodes.