Hi,
we're currently evaluating KairosDB for time series which looks quite
nice.
https://kairosdb.github.io/
The cool thing with KairosDB is that it uses Cassandra as storage engine
and provide
additional features (mainly a REST-based API for accessing data).
Maybe you can take a look the schema definition kairos uses for
cassandra and
check if it suits you. (Or use it directly as it stores data in
cassandra anyway).
Greetings,
Michael
PS: Oh and GRAFANA has a kairosdb connector so you can test queries and
create dashboards fast.
On 18.06.2018 09:46, Affan Syed wrote:
I have looked at this problem for a good year now. My feel is that
Cassandra alone as the sole underlying DB for Timeseries just does not
cut it.
I am starting to look at C* along with another DB for executing the
sort of queries we want here.
Currently I am evaluating Druid vs Kudu to be this supportive DB. Any
comments from community? Cassandra would more be for storage and
backup, while the data denormalization effort is taken care of by
another DB.
thank you
- Affan
On Thu, Jul 27, 2017 at 1:38 AM, CPC <acha...@gmail.com> wrote:
If all of your queries like this(i mean get all devices given a a
time range) Hadoop would be more appropriate since those are
analytical queries.
Anyway, to query such data with spark Cassandra connector your
partition key could include day and hash of your deviceid as pseudo
partition key column (could be abs(murmur(deviceid)%500) we add this
column to distribute data more evenly) . When you want query a time
range you should generate a rdd of tuple2 with all days that
intersect with that date and for each day your rdd should include
0..500 range. Like:
(20170726,0)
(20170726,1)
.
.
.
(20170726,499)
Then you should join this rdd with your table using
joinwithcassandratable method.
On Jul 26, 2017 4:41 PM, "Junaid Nasir" <jna...@an10.io> wrote:
all devices.
After selecting the data I group them and perform other actions i.e
sum, avg on fields and then display those to compare how devices are
doing compared to each other.
On Wed, Jul 26, 2017 at 5:32 PM, CPC <acha...@gmail.com> wrote:
Hi Junaid,
Given a time range do you want to take all devices or a specific
device?
On Jul 26, 2017 3:15 PM, "Junaid Nasir" <jna...@an10.io> wrote:
I have a C* cluster (3 nodes) with some 60gb data (replication
factor 2). when I started using C* coming from SQL background didn't
give much thought about modeling the data correctly. so what I did
was
CREATE TABLE data ( deviceId int,
time timestamp,
field1 text,
filed2 text,
field3 text,
PRIMARY KEY(deviceId, time)) WITH CLUSTERING
ORDER BY (time ASC);
but most of the queries I run (using spark and datastax connector)
compares data of different devices for some time period. for example
SELECT * FROM data WHERE time > '2017-07-01 12:00:00';
from my understanding this runs a full table scan. as shown in spark
UI (from DAG visualization "Scan
org.apache.spark.sql.cassandra.CassandraSourceRelation@32bb7d65")
meaning C* will read all the data and then filter for time. Spark
jobs runs for hours even for smaller time frames.
what is the right approach for data modeling for such queries?. I
want to get a general idea of things to look for when modeling such
data.
really appreciate all the help from this community :). if you need
any extra details please ask me here.
Regards,
Junaid
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