I am trying to keep track of the amount of events of each type that occured in one-hour buckets of time, and then sum the counts per category in arbitrary time ranges. So, I create a table like this:
CREATE TABLE IF NOT EXISTS sensor_activity_stats(
sensor_id text,
datetime_hour_bucket timestamp,
activity_type text,
activity_count counter,
PRIMARY KEY ((sensor_id), datetime_hour_bucket, activity_type)
)
WITH CLUSTERING ORDER BY(datetime_hour_bucket DESC, activity_type ASC);
I would like to be able to achieve this kind of query:
SELECT datetime_hour_bucket, activity_type, SUM(activity_count) as count
FROM sensor_activity_stats
WHERE sensor_id=:sensorId
AND datetime_hour_bucket >= :fromDate AND datetime_hour_bucket < :untilDate
GROUP BY activity_type
Cassandra complains about because grouping must be done in the order of the primary key columns. And, if I change the order I won't be able to query by a range over any activity_type.
Some notes:
I am grouping by hours because some users could ask me to show the data in different timezones and I want to be able to perform a decent conversion.
The activity_type has low cardinality, however I can not be sure I'll always be able to predict it's possible values.
Right now my solution was to query the whole data in the range and perform the aggregation myself in code. Have you have faced similar situation and what was your solution? Would you suggest a different way of querying or arranging the data?
I hope you've found the solution of your problem, however I have a way to you try.
First, you can chage the create table to change the order of fields:
CREATE TABLE IF NOT EXISTS sensor_activity_stats(
sensor_id text,
datetime_hour_bucket timestamp,
activity_type text,
activity_count counter,
PRIMARY KEY (activity_type, sensor_id, datetime_hour_bucket, activity_count)
)
WITH CLUSTERING ORDER BY(activity_type ASC, datetime_hour_bucket DESC);
Then, the query you can add the field "datetime_hour_bucket" in the Group By clause:
SELECT datetime_hour_bucket, activity_type, SUM(activity_count) as count
FROM sensor_activity_stats
WHERE sensor_id=:sensorId
AND datetime_hour_bucket >= :fromDate AND datetime_hour_bucket < :untilDate
GROUP BY activity_type, datetime_hour_bucket;
Related
I am already able to get the last row of time-series table as:
SELECT * from myapp.locations WHERE organization_id=1 and user_id=15 and date='2017-2-22' ORDER BY unix_time DESC LIMIT 1;
That works fine, however, I am wondering about performance and overhead of executing ORDER BY as rows are already sorted, I just use it to get the last row, is it an overhead in my case?
If I don't use ORDER BY, I will always get the first row in the table, so, I though I might be able to use INSERT in another way, ex: insert always in the beginning instead of end of table?
Any advice? shall I use ORDER BY without worries about performance?
Just define your clustering key order to DESC
Like the below schema :
CREATE TABLE locations (
organization_id int,
user_id int,
date text,
unix_time bigint,
lat double,
long double,
PRIMARY KEY ((organization_id, user_id, date), unix_time)
) WITH CLUSTERING ORDER BY (unix_time DESC);
So by default your data will sorted by unix_time desc, you don't need to specify in query
Now you can just use the below query to get the last row :
SELECT * from myapp.locations WHERE organization_id = 1 and user_id = 15 and date = '2017-2-22' LIMIT 1;
If your query pattern for that table is always ORDER BY unix_time DESC then you are in a reverse order time-series scenario, and I can say that your model is inaccurate (not wrong).
There's no reason not to sort the records in reverse order by adding a WITH CLUSTERING ORDER BY unix_time DESC in the table definition, and in my opinion the ORDER BY unix_time DESC will perform at most on par with something explicitly meant for these use cases (well, I think it will perform worse).
I have a table,
CREATE TABLE log (
uuid uuid,
time timeuuid,
user text,
....
PRIMARY KEY (uuid, time, user)
)
and
CREATE CUSTOM INDEX time_idx on Log(time) USING 'org.apache.cassandra.index.sasi.SASIIndex';
then I want to select base on time
select * from Log where time > 84bfd880-b001-11e6-918c-24eda6ab1677;
and nothing return, it will return if I use equal(=). Which step did I go wrong ?
You need to make time_idx index as SPARSE index.
The SPARSE index is meant to improve performance of querying large, dense number ranges like timestamps for data inserted every millisecond. If the data is numeric, millions of columns values with a small number of partition keys characterize the data, and range queries will be performed against the index, then SPARSE is the best choice. For numeric data that does not meet this criteria, PREFIX is the best choice.
drop the time_idx and create with the below query
CREATE CUSTOM INDEX time_idx on Log(time) USING 'org.apache.cassandra.index.sasi.SASIIndex' WITH OPTIONS = { 'mode': 'SPARSE' };
Now you can query with The inequalities >=, > and <= .
Limitation : SPARSE indexing is used only for numeric data, so LIKE queries do not apply.
and Another thing your table creation is not right. It should be
CREATE TABLE log (
uuid uuid,
time timeuuid,
user text,
PRIMARY KEY (uuid, time, user)
)
I'm currently trying to model a column family that has two timestamps specifying whether an entry is valid (or 'active') at a given date (typically execution time).
No big issue with traditional SQL, 64 gigs of RAM and some indices, we're doing that quite often with our SQL server.
However, in CQL I haven't managed to model this scenario and write valid queries for it.
My basic model is (I skipped the PK definition!)
create table myTable(
id uuid,
validFrom timeuuid,
validTo timeuuid,
someInformationalData varChar
);
Some explanations:
due to the fact, that a validity date is not unique, I need a combined key in my final application this is going to be a usergroup reference (would be an ideal partition key)
validFrom/To are designed to be optional, but I could deal with by using boundary values (1970, 2038) for 'null' values passed through the persistence layer
I tried various combinations of partitioning/clustering keys, however neither of them resulted in valid CQL
-- only active results
select *
from
myTable
where
validFrom < now()
and
validTo > now()
I'm quite new to the NoSQL/CQL world and am struggling a bit with converting some of our applications. I could do it in memory, but I'm afraid, this could get a bottleneck at some point...
No sure if this kind of 'I have no idea what I'm doing' yell is appropriate, but any kind of help would be appreciated. :)
edit Here's one of the approaches I've been messing around with
drop table if exists myTable;
create table myTable(
id int,
datefrom timeuuid,
dateto timeuuid,
someColumns varChar,
primary key((id,datefrom),dateto)
);
create index if not exists my_idx on myTable(datefrom);
insert into myTable(id, datefrom,dateto,somecolumns)
values(0,minTimeuuid('1970-01-01 00:00:00'),minTimeuuid('2020-01-01 00:00:00'),'test');
insert into myTable(id,datefrom,dateto,somecolumns)
values(1,minTimeuuid('1970-01-01 00:00:00'),minTimeuuid('2012-01-01 00:00:00'),'test2');
select * from myTable where dateto > now() allow filtering;
-- invalid ("A column of a partition key can be restricted only if the preceding one is restricted by an Equal relation.")
select * from myTable where datefrom < now() and dateto > now() allow filtering;
The first query is limiting my result, the row with 'validTo=2012-01-01' is filtered, but I wasn't able to work out a scheme that worked on both limitations in the where clause.
If I understand your problem, what you are looking for is a way to run a range query based on the timestamp. Basically to be able to do this, your model will have to have the timestamp component as part of the clustering key:
create table myTable(
eventType uuid,
ts timestamp,
val text,
PRIMARY KEY (eventType, ts)
);
The above will allow you to run a query like: SELECT eventType, val from myTable where eventType = 'your_event' and ts >= 'start_ts' and ts < 'end_ts'.
What you need to remember is that the clustering keys are dictating the order on disk, thus making it possible to run efficiently queries like above. You can read more details about this in the CQL spec SELECT section.
Their is no such thing as Now() in cassandra like any other sql databases. you have to clearly mention today's date instead of Now() ..
You can use columns in which you defined as primary key or secondary index in where clause.
I'm new to cassandra and would like to ask what would be correct model design pattern for such tasks.
I would like to model data with future removal possibility.
I have 100,000,000 records per day of this structure:
transaction_id <- this is unique
transaction_time
transaction_type
user_name
... some other information
I will need to fetch data by user_name (I have about 5,000,000 users).
Also I will need to find transaction details by its id.
All the data will be irrelevant after say about 30 days, so need to find a way to delete outdated rows.
As much I have found, TTL-s expire column values, not rows.
So far I came across with this model, and as I understand it will imply really wide rows:
CREATE TABLE user_transactions (
transaction_date timestamp, //date part of transactiom
user_name text,
transaction_id text,
transaction_time timestamp, //original transaction time
transaction_type int,
PRIMARY KEY ((transaction_date, user_name), transaction_id)
);
CREATE INDEX idx_user_transactions_uname ON USER_TRANSACTIONS(user_name);
CREATE INDEX idx_user_transactions_tid ON USER_TRANSACTIONS(transaction_id);
but this model does not allow deletions by transaction_date.
this also builds indexes with high cardinality, what cassandra docs strongly discourages
So what will be the correct model for this task?
EDIT:
Ugly workaround I came with so far is to create single table per date partition. Mind you, I call this workaround and not a solution. I'm still looking for right data model
CREATE TABLE user_transactions_YYYYMMDD (
user_name text,
transaction_id text,
transaction_time timestamp,
transaction_type int,
PRIMARY KEY (user_name)
);
YYYYMMDD is date part of transaction. we can create similar table with transaction_id for transaction lookup. obsolete tables can be dropped or truncated.
Maybe you should denormalized your data model. For example to query by user_name you can use a cf like this:
CREATE TABLE user_transactions (
transaction_date timestamp, //date part of transactiom
user_name text,
transaction_id text,
transaction_time timestamp, //original transaction time
transaction_type int,
PRIMARY KEY (user_name, transaction_id)
);
So you can query using the partition key directly like this:
SELECT * FROM user_transactions WHERE user_name = 'USER_NAME';
And for the id you can use a cf like this:
CREATE TABLE user_transactions (
transaction_date timestamp, //date part of transactiom
user_name text,
transaction_id text,
transaction_time timestamp, //original transaction time
transaction_type int,
PRIMARY KEY (transaction_id)
);
so the query could be something like this:
SELECT * FROM user_transactions WHERE transaction_id = 'ID';
By this way you dont need indexes.
About the TTL, maybe you could programatically ensure that you update all the columns in the row at the same time (same cql sentence).
Perhaps my answer will be a little useful.
I would have done so:
CREATE TABLE user_transactions (
date timestamp,
user_name text,
id text,
type int,
PRIMARY KEY (id)
);
CREATE INDEX idx_user_transactions_uname ON user_transactions (user_name);
No need in 'transaction_time timestamp', because this time will be set by Cassandra to each column, and can be fetched by WRITETIME(column name) function. Because you write all the columns simultaneously, then you can call this function on any column.
INSERT INTO user_transactions ... USING TTL 86400;
will expire all columns simultaneously. So do not worry about deleting rows. See here: Expiring columns.
But as far as I know, you can not delete an entire row - key column still remains, and in the other columns will be written NULL.
If you want to delete the rows manually, or just want to have an estimate of rows to be deleted by a TTL, then I recommend driver Astyanax: AllRowsReader All rows query.
And indeed as a driver to work with Cassandra I recommend you use Astyanax.
I have a column family with primary key definition like this:
...
PRIMARY KEY ((website_id, item_id), user_id, date)
which will be queried using queries such as:
SELECT * FROM myCF
WHERE website_id = 30 AND item_id = 10
AND user_id = 0 AND date > 'some_date' ;
However, I'd like to keep my column family ordered by date only, such as SELECT date FROM myCF ; would return the most recent inserted date.
Due to the order of clustering columns, what I get is an order per user_id then per date.
If I change the primary key definition to:
PRIMARY KEY ((website_id, item_id), date, user_id)
I can no longer run the same query, as date must be restricted is user_id is.
I thought there might be some way to say:
...
PRIMARY KEY ((website_id, shop_id), store_id, date)
) WITH CLUSTERING ORDER BY (store_id RANDOMPLEASE, date DESC) ;
But it doesn't seem to exist. Worst, maybe this is completely stupid and I don't get why.
Is there any ways of achieving this? Am I missing something?
Many thanks!
Your query example restricts user_id so that should work with the second table format. But if you are actually trying to run queries like
SELECT * FROM myCF
WHERE website_id = 30 AND item_id = 10
AND date > 'some_date'
Then you need an additional table which is created to handle those queries, it would only order on Date and not on user id
Create Table LookupByDate ... PRIMARY KEY ((website_id, item_id), date)
In addition to your primary query, if all you try to get is "return the most recent inserted date", you may not need an additional table. You can use "static column" to store the last update time per partition. CASSANDRA-6561
It probably won't help your particular case (since I imagine your list of all users is unmanagably large), but if the condition on the first clustering column is matching one of a relatively small set of values then you can use IN.
SELECT * FROM myCF
WHERE website_id = 30 AND item_id = 10
AND user_id IN ? AND date > 'some_date'
Don't use IN on the partition key because this will create an inefficient query that hits multiple nodes putting stress on the coordinator node. Instead, execute multiple asynchronous queries in parallel. But IN on a clustering column is absolutely fine.