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Cassandra Day NYC - Cassandra anti patterns

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Christopher Batey
Christopher Batey

This document discusses various strategies for modeling and querying data in Cassandra to avoid common anti-patterns like client-side joins, multi-partition queries, and mutable data. It recommends denormalizing data and using techniques like bucketing and user-defined types to reduce the need for joins. Batch statements are recommended only for writing to the same partition, as writing to multiple partitions in a batch can degrade performance. Logged batches can be used to write related data to multiple tables in an atomic way. The document also discusses an event sourcing approach to modeling mutable data.

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©2013 DataStax Confidential. Do not distribute without consent. @chbatey Christopher Batey Staying in love with Cassandra
@chbatey Anti patterns • Client side joins • Multi partition queries • Batches • Mutable data • Secondary indices • Retry policies?
@chbatey Disclaimer • I’m going to be pedantic • I’m assuming you want to scale
@chbatey Distributed joins
@chbatey Where’s the data? Client Where do I go for the max?
@chbatey Storing customer events • Customer event - customer_id - ChrisBatey - staff_id - Charlie - event_type - login, logout, add_to_basket, remove_from_basket - time • Store - name - store_type - Website, PhoneApp, Phone, Retail - location
@chbatey Model CREATE TABLE customer_events( customer_id text, staff_id text, time timeuuid, event_type text, store_name text, PRIMARY KEY ((customer_id), time)); CREATE TABLE store( store_name text, location text, store_type text, PRIMARY KEY (store_name));
@chbatey Reading this client RF3 C Read latest customer event CL = QUORUM Read store information C
@chbatey One to one vs one to many • This required possibly 6 out of 8 of our nodes to be up • Imagine if there were multiple follow up queries
@chbatey Multi-partition queries • Client side joins normally end up falling into the more general anti-pattern of multi partition queries
@chbatey Adding staff link CREATE TABLE customer_events( customer_id text, staff set<text>, time timeuuid, event_type text, store_name text, PRIMARY KEY ((customer_id), time)); CREATE TYPE staff( name text, favourite_colour text, job_title text); CREATE TYPE store( store_name text, location text, store_type text);
@chbatey Queries select * from customer_events where customer_id = ‘chbatey’ limit 1 select * from staff where name in (staff1, staff2, staff3, staff4) Any one of these fails the whole query fails
@chbatey Use a multi node cluster locally
@chbatey Use a multi node cluster locally Status=Up/Down |/ State=Normal/Leaving/Joining/Moving -- Address Load Tokens Owns Host ID Rack UN 127.0.0.1 102.27 KB 256 ? 15ad7694-3e76-4b74-aea0-fa3c0fa59532 rack1 UN 127.0.0.2 102.18 KB 256 ? cca7d0bb-e884-49f9-b098-e38fbe895cbc rack1 UN 127.0.0.3 93.16 KB 256 ? 1f9737d3-c1b8-4df1-be4c-d3b1cced8e30 rack1 UN 127.0.0.4 102.1 KB 256 ? fe27b958-5d3a-4f78-9880-76cb7c9bead1 rack1 UN 127.0.0.5 93.18 KB 256 ? 66eb3f23-8889-44d6-a9e7-ecdd57ed61d0 rack1 UN 127.0.0.6 102.12 KB 256 ? e2e99a7b-c1fb-4f2a-9e4f-7a4666f8245e rack1
@chbatey Let’s see with with a 6 node cluster INSERT INTO staff (name, favourite_colour , job_title ) VALUES ( 'chbatey', 'red', 'Technical Evangelist' ); INSERT INTO staff (name, favourite_colour , job_title ) VALUES ( 'luket', 'red', 'Technical Evangelist' ); INSERT INTO staff (name, favourite_colour , job_title ) VALUES ( 'jonh', 'blue', 'Technical Evangelist' ); select * from staff where name in ('chbatey', 'luket', 'jonh');
@chbatey Trace with CL ONE = 4 nodes used Execute CQL3 query | 2015-02-02 06:39:58.759000 | 127.0.0.1 | 0 Parsing select * from staff where name in ('chbatey', 'luket', 'jonh'); [SharedPool-Worker-1] | 2015-02-02 06:39:58.766000 | 127.0.0.1 | 7553 Preparing statement [SharedPool-Worker-1] | 2015-02-02 06:39:58.768000 | 127.0.0.1 | 9249 Executing single-partition query on staff [SharedPool-Worker-3] | 2015-02-02 06:39:58.773000 | 127.0.0.1 | 14255 Sending message to /127.0.0.3 [WRITE-/127.0.0.3] | 2015-02-02 06:39:58.773001 | 127.0.0.1 | 14756 Sending message to /127.0.0.5 [WRITE-/127.0.0.5] | 2015-02-02 06:39:58.773001 | 127.0.0.1 | 14928 Sending message to /127.0.0.3 [WRITE-/127.0.0.3] | 2015-02-02 06:39:58.774000 | 127.0.0.1 | 16035 Executing single-partition query on staff [SharedPool-Worker-1] | 2015-02-02 06:39:58.777000 | 127.0.0.5 | 1156 Enqueuing response to /127.0.0.1 [SharedPool-Worker-1] | 2015-02-02 06:39:58.777001 | 127.0.0.5 | 1681 Sending message to /127.0.0.1 [WRITE-/127.0.0.1] | 2015-02-02 06:39:58.778000 | 127.0.0.5 | 1944 Executing single-partition query on staff [SharedPool-Worker-1] | 2015-02-02 06:39:58.778000 | 127.0.0.3 | 1554 Processing response from /127.0.0.5 [SharedPool-Worker-3] | 2015-02-02 06:39:58.779000 | 127.0.0.1 | 20762 Enqueuing response to /127.0.0.1 [SharedPool-Worker-1] | 2015-02-02 06:39:58.779000 | 127.0.0.3 | 2425 Sending message to /127.0.0.5 [WRITE-/127.0.0.5] | 2015-02-02 06:39:58.779000 | 127.0.0.1 | 21198 Sending message to /127.0.0.1 [WRITE-/127.0.0.1] | 2015-02-02 06:39:58.779000 | 127.0.0.3 | 2639 Sending message to /127.0.0.6 [WRITE-/127.0.0.6] | 2015-02-02 06:39:58.779000 | 127.0.0.1 | 21208 Executing single-partition query on staff [SharedPool-Worker-1] | 2015-02-02 06:39:58.780000 | 127.0.0.5 | 304 Enqueuing response to /127.0.0.1 [SharedPool-Worker-1] | 2015-02-02 06:39:58.780001 | 127.0.0.5 | 574 Executing single-partition query on staff [SharedPool-Worker-2] | 2015-02-02 06:39:58.781000 | 127.0.0.3 | 4075 Sending message to /127.0.0.1 [WRITE-/127.0.0.1] | 2015-02-02 06:39:58.781000 | 127.0.0.5 | 708 Enqueuing response to /127.0.0.1 [SharedPool-Worker-2] | 2015-02-02 06:39:58.781001 | 127.0.0.3 | 4348 Sending message to /127.0.0.1 [WRITE-/127.0.0.1] | 2015-02-02 06:39:58.782000 | 127.0.0.3 | 5371 Executing single-partition query on staff [SharedPool-Worker-1] | 2015-02-02 06:39:58.783000 | 127.0.0.6 | 2463 Enqueuing response to /127.0.0.1 [SharedPool-Worker-1] | 2015-02-02 06:39:58.784000 | 127.0.0.6 | 2905 Sending message to /127.0.0.1 [WRITE-/127.0.0.1] | 2015-02-02 06:39:58.784001 | 127.0.0.6 | 3160 Processing response from /127.0.0.6 [SharedPool-Worker-2] | 2015-02-02 06:39:58.785000 | 127.0.0.1 | -- Request complete | 2015-02-02 06:39:58.782995 | 127.0.0.1 | 23995
@chbatey Denormalise with UDTs CREATE TYPE store (name text, type text, postcode text) CREATE TYPE staff (name text, fav_colour text, job_title text) 
 CREATE TABLE customer_events( customer_id text, time timeuuid, event_type text, store store, staff set<staff>, PRIMARY KEY ((customer_id), time)); User defined types Cassandra 2.1 has landed in DSE 4.7!!!
@chbatey Less obvious example • A good pattern for time series: Bucketing
@chbatey Adding a time bucket CREATE TABLE customer_events_bucket( customer_id text, time_bucket text time timeuuid, event_type text, store store, staff set<staff>, PRIMARY KEY ((customer_id, time_bucket), time));
@chbatey Queries select * from customer_events_bucket where customer_id = ‘chbatey’ and time_bucket IN (‘2015-01-01|0910:01’, ‘2015-01-01|0910:02’, ‘2015-01-01|0910:03’, ‘2015-01-01| 0910:04’) Often better as multiple async queries
@chbatey Tips for avoiding joins & multi gets • Say no to client side joins by denormalising - Much easier with UDTs • When bucketing aim for at most two buckets for a query • Get in the habit of reading trace + using a multi node cluster locally
@chbatey Unlogged Batches
@chbatey Unlogged Batches • Unlogged batches - Send many statements as one
@chbatey Customer events table CREATE TABLE if NOT EXISTS customer_events ( customer_id text, statff_id text, store_type text, time timeuuid , event_type text, PRIMARY KEY (customer_id, time))
@chbatey Inserting events INSERT INTO events.customer_events (customer_id, time , event_type , statff_id , store_type ) VALUES ( ?, ?, ?, ?, ?) public void storeEvent(ConsistencyLevel consistencyLevel, CustomerEvent customerEvent) {
 BoundStatement boundInsert = insertStatement.bind( customerEvent.getCustomerId(), customerEvent.getTime(), customerEvent.getEventType(), customerEvent.getStaffId(), customerEvent.getStaffId());
 
 boundInsert.setConsistencyLevel(consistencyLevel);
 session.execute(boundInsert);
 }
@chbatey Batch insert - Good idea? public void storeEvents(ConsistencyLevel consistencyLevel, CustomerEvent... events) {
 BatchStatement batchStatement = new BatchStatement(BatchStatement.Type.UNLOGGED);
 
 for (CustomerEvent event : events) { BoundStatement boundInsert = insertStatement.bind( customerEvent.getCustomerId(), customerEvent.getTime(), customerEvent.getEventType(), customerEvent.getStaffId(), customerEvent.getStaffId());
 
 batchStatement.add(boundInsert); } session.execute(batchStatement); }
@chbatey Not so much client C Very likely to fail if nodes are down / over loaded Gives far too much work for the coordinator Ruins performance gains of token aware driver
@chbatey Individual queries client
@chbatey Unlogged batch use case public void storeEvents(String customerId, ConsistencyLevel consistencyLevel, CustomerEvent... events) {
 BatchStatement batchStatement = new BatchStatement(BatchStatement.Type.UNLOGGED);
 batchStatement.enableTracing();
 
 for (CustomerEvent event : events) {
 BoundStatement boundInsert = insertStatement.bind(
 customerId,
 event.getTime(),
 event.getEventType(),
 event.getStaffId(),
 event.getStaffId());
 boundInsert.enableTracing();
 boundInsert.setConsistencyLevel(consistencyLevel);
 batchStatement.add(boundInsert);
 }
 
 ResultSet execute = session.execute(batchStatement);
 logTraceInfo(execute.getExecutionInfo());
 } Partition Key!!
@chbatey Writing this client C All for the same partition :)
@chbatey Logged Batches
@chbatey Logged Batches • Once accepted the statements will eventually succeed • Achieved by writing them to a distributed batchlog • 30% slower than unlogged batches
@chbatey Batch insert - Good idea? public void storeEvents(ConsistencyLevel consistencyLevel, CustomerEvent... events) {
 BatchStatement batchStatement = new BatchStatement(BatchStatement.Type.LOGGED);
 
 for (CustomerEvent event : events) { BoundStatement boundInsert = insertStatement.bind( customerEvent.getCustomerId(), customerEvent.getTime(), customerEvent.getEventType(), customerEvent.getStaffId(), customerEvent.getStaffId());
 
 batchStatement.add(boundInsert); } session.execute(batchStatement); }
@chbatey Not so much client C BATCH LOG BL-R BL-R BL-R: Batch log replica
@chbatey Use case? CREATE TABLE if NOT EXISTS customer_events ( customer_id text, statff_id text, store_type text, time timeuuid , event_type text, PRIMARY KEY (customer_id, time)) CREATE TABLE if NOT EXISTS customer_events_by_staff ( customer_id text, statff_id text, store_type text, time timeuuid , event_type text, PRIMARY KEY (staff_id, time))
@chbatey Storing events to both tables in a batch public void storeEventLogged(ConsistencyLevel consistencyLevel, CustomerEvent customerEvent) {
 BoundStatement boundInsertForCustomerId = insertByCustomerId.bind(customerEvent.getCustomerId(), customerEvent.getTime(), customerEvent.getEventType(), customerEvent.getStaffId(), customerEvent.getStaffId());
 BoundStatement boundInsertForStaffId = insertByStaffId.bind(customerEvent.getCustomerId(), customerEvent.getTime(), customerEvent.getEventType(), customerEvent.getStaffId(), customerEvent.getStaffId()); 
 BatchStatement batchStatement = new BatchStatement(BatchStatement.Type.LOGGED);
 batchStatement.enableTracing();
 batchStatement.setConsistencyLevel(consistencyLevel);
 batchStatement.add(boundInsertForCustomerId);
 batchStatement.add(boundInsertForStaffId);
 
 
 ResultSet execute = session.execute(batchStatement);
 }
@chbatey Mutable data
@chbatey Distributed mutable state :-( create TABLE accounts(customer text PRIMARY KEY, balance_in_pence int); Overly naive example • Cassandra uses last write wins (no Vector clocks) • Read before write is an anti-pattern - race conditions and latency
@chbatey Take a page from Event sourcing create table accounts_log( customer text, time timeuuid, delta int, primary KEY (customer, time)); All records for the same customer in the same storage row Transactions ordered by TIMEUUID - UUIDs are your friends in distributed systems
@chbatey Tips • Avoid mutable state in distributed system, favour immutable log • Can roll up and snapshot to avoid calculation getting too big • If you really have to then checkout LWTs and do via CAS - this will be slower
@chbatey Secondary Indices
@chbatey Customer events table CREATE TABLE if NOT EXISTS customer_events ( customer_id text, staff_id text, store_type text, time timeuuid , event_type text, PRIMARY KEY (customer_id, time)) create INDEX on customer_events (staff_id) ;
@chbatey Indexes to the rescue? customer_id time staff_id chbatey 2015-03-03 08:52:45 trevor chbatey 2015-03-03 08:52:54 trevor chbatey 2015-03-03 08:53:11 bill chbatey 2015-03-03 08:53:18 bill rusty 2015-03-03 08:56:57 bill rusty 2015-03-03 08:57:02 bill rusty 2015-03-03 08:57:20 trevor staff_id customer_id trevor chbatey trevor chbatey bill chbatey bill chbatey bill rusty bill rusty trevor rusty
@chbatey Indexes to the rescue? staff_id customer_id trevor chbatey trevor chbatey bill chbatey bill chbatey staff_id customer_id bill rusty bill rusty trevor rusty A B chbatey rusty customer_id time staff_id chbatey 2015-03-03 08:52:45 trevor chbatey 2015-03-03 08:52:54 trevor chbatey 2015-03-03 08:53:11 bill chbatey 2015-03-03 08:53:18 bill rusty 2015-03-03 08:56:57 bill rusty 2015-03-03 08:57:02 bill rusty 2015-03-03 08:57:20 trevor customer_events table staff_id customer_id trevor chbatey trevor chbatey bill chbatey bill chbatey bill rusty bill rusty trevor rusty staff_id index
@chbatey Do it your self index CREATE TABLE if NOT EXISTS customer_events ( customer_id text, statff_id text, store_type text, time timeuuid , event_type text, PRIMARY KEY (customer_id, time)) CREATE TABLE if NOT EXISTS customer_events_by_staff ( customer_id text, statff_id text, store_type text, time timeuuid , event_type text, PRIMARY KEY (staff_id, time))
@chbatey Anti patterns summary • Most anti patterns are very obvious in trace output • Don’t test on small clusters, most problems only occur at scale
@chbatey Understanding failures (crazy retry policies)
@chbatey Write timeout Application C R1 R2 R3 C=QUROUM Replication factor: 3 timeout timeout Write timeout
@chbatey Retrying writes • Cassandra does not roll back! • R3 has the data and the coordinator has hinted for R1 and R2
@chbatey Write timeout • Received acknowledgements • Required acknowledgements • Consistency level • CAS and Batches are more complicated: - WriteType: SIMPLE, BATCH, UNLOGGED_BATCH, BATCH_LOG, CAS
@chbatey Batches • BATCH_LOG - Timed out waiting for batch log replicas • BATCH - Written to batch log but timed out waiting for actual replica - Will eventually be committed
@chbatey Idempotent writes • All writes are idempotent with the following exceptions: - Counters - lists
@chbatey Anti patterns summary • Most anti patterns are very obvious in trace output • Don’t test on small clusters, most problems only occur at scale
@chbatey Cassandra as a Queue (tombstones)
@chbatey Well documented anti-pattern • http://www.datastax.com/dev/blog/cassandra-anti- patterns-queues-and-queue-like-datasets • http://lostechies.com/ryansvihla/2014/10/20/domain- modeling-around-deletes-or-using-cassandra-as-a- queue-even-when-you-know-better/
@chbatey Requirements • Produce data in one DC • Consume it once and delete from another DC • Use Cassandra?
@chbatey Datamodel CREATE TABLE queues ( name text, enqueued_at timeuuid, payload blob, PRIMARY KEY (name, enqueued_at) ); SELECT enqueued_at, payload FROM queues WHERE name = 'queue-1' LIMIT 1;
@chbatey Trace activity | source | elapsed -------------------------------------------+-----------+-------- execute_cql3_query | 127.0.0.3 | 0 Parsing statement | 127.0.0.3 | 48 Peparing statement | 127.0.0.3 | 362 Message received from /127.0.0.3 | 127.0.0.1 | 42 Sending message to /127.0.0.1 | 127.0.0.3 | 718 Executing single-partition query on queues | 127.0.0.1 | 145 Acquiring sstable references | 127.0.0.1 | 158 Merging memtable contents | 127.0.0.1 | 189 Merging data from memtables and 0 sstables | 127.0.0.1 | 235 Read 1 live and 19998 tombstoned cells | 127.0.0.1 | 251102 Enqueuing response to /127.0.0.3 | 127.0.0.1 | 252976 Sending message to /127.0.0.3 | 127.0.0.1 | 253052 Message received from /127.0.0.1 | 127.0.0.3 | 324314 Not good :(
@chbatey Tips • Avoid lots of deletes + range queries in the same partition • Avoid it with data modelling / query pattern: - Move partition - Add a start for your range: e.g. enqueued_at > 9d1cb818-9d7a-11b6-96ba-60c5470cbf0e

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Cassandra Day NYC - Cassandra anti patterns

  • 1. ©2013 DataStax Confidential. Do not distribute without consent. @chbatey Christopher Batey Staying in love with Cassandra
  • 2. @chbatey Anti patterns • Client side joins • Multi partition queries • Batches • Mutable data • Secondary indices • Retry policies?
  • 3. @chbatey Disclaimer • I’m going to be pedantic • I’m assuming you want to scale
  • 6. @chbatey Storing customer events • Customer event - customer_id - ChrisBatey - staff_id - Charlie - event_type - login, logout, add_to_basket, remove_from_basket - time • Store - name - store_type - Website, PhoneApp, Phone, Retail - location
  • 7. @chbatey Model CREATE TABLE customer_events( customer_id text, staff_id text, time timeuuid, event_type text, store_name text, PRIMARY KEY ((customer_id), time)); CREATE TABLE store( store_name text, location text, store_type text, PRIMARY KEY (store_name));
  • 8. @chbatey Reading this client RF3 C Read latest customer event CL = QUORUM Read store information C
  • 9. @chbatey One to one vs one to many • This required possibly 6 out of 8 of our nodes to be up • Imagine if there were multiple follow up queries
  • 10. @chbatey Multi-partition queries • Client side joins normally end up falling into the more general anti-pattern of multi partition queries
  • 11. @chbatey Adding staff link CREATE TABLE customer_events( customer_id text, staff set<text>, time timeuuid, event_type text, store_name text, PRIMARY KEY ((customer_id), time)); CREATE TYPE staff( name text, favourite_colour text, job_title text); CREATE TYPE store( store_name text, location text, store_type text);
  • 12. @chbatey Queries select * from customer_events where customer_id = ‘chbatey’ limit 1 select * from staff where name in (staff1, staff2, staff3, staff4) Any one of these fails the whole query fails
  • 13. @chbatey Use a multi node cluster locally
  • 14. @chbatey Use a multi node cluster locally Status=Up/Down |/ State=Normal/Leaving/Joining/Moving -- Address Load Tokens Owns Host ID Rack UN 127.0.0.1 102.27 KB 256 ? 15ad7694-3e76-4b74-aea0-fa3c0fa59532 rack1 UN 127.0.0.2 102.18 KB 256 ? cca7d0bb-e884-49f9-b098-e38fbe895cbc rack1 UN 127.0.0.3 93.16 KB 256 ? 1f9737d3-c1b8-4df1-be4c-d3b1cced8e30 rack1 UN 127.0.0.4 102.1 KB 256 ? fe27b958-5d3a-4f78-9880-76cb7c9bead1 rack1 UN 127.0.0.5 93.18 KB 256 ? 66eb3f23-8889-44d6-a9e7-ecdd57ed61d0 rack1 UN 127.0.0.6 102.12 KB 256 ? e2e99a7b-c1fb-4f2a-9e4f-7a4666f8245e rack1
  • 15. @chbatey Let’s see with with a 6 node cluster INSERT INTO staff (name, favourite_colour , job_title ) VALUES ( 'chbatey', 'red', 'Technical Evangelist' ); INSERT INTO staff (name, favourite_colour , job_title ) VALUES ( 'luket', 'red', 'Technical Evangelist' ); INSERT INTO staff (name, favourite_colour , job_title ) VALUES ( 'jonh', 'blue', 'Technical Evangelist' ); select * from staff where name in ('chbatey', 'luket', 'jonh');
  • 16. @chbatey Trace with CL ONE = 4 nodes used Execute CQL3 query | 2015-02-02 06:39:58.759000 | 127.0.0.1 | 0 Parsing select * from staff where name in ('chbatey', 'luket', 'jonh'); [SharedPool-Worker-1] | 2015-02-02 06:39:58.766000 | 127.0.0.1 | 7553 Preparing statement [SharedPool-Worker-1] | 2015-02-02 06:39:58.768000 | 127.0.0.1 | 9249 Executing single-partition query on staff [SharedPool-Worker-3] | 2015-02-02 06:39:58.773000 | 127.0.0.1 | 14255 Sending message to /127.0.0.3 [WRITE-/127.0.0.3] | 2015-02-02 06:39:58.773001 | 127.0.0.1 | 14756 Sending message to /127.0.0.5 [WRITE-/127.0.0.5] | 2015-02-02 06:39:58.773001 | 127.0.0.1 | 14928 Sending message to /127.0.0.3 [WRITE-/127.0.0.3] | 2015-02-02 06:39:58.774000 | 127.0.0.1 | 16035 Executing single-partition query on staff [SharedPool-Worker-1] | 2015-02-02 06:39:58.777000 | 127.0.0.5 | 1156 Enqueuing response to /127.0.0.1 [SharedPool-Worker-1] | 2015-02-02 06:39:58.777001 | 127.0.0.5 | 1681 Sending message to /127.0.0.1 [WRITE-/127.0.0.1] | 2015-02-02 06:39:58.778000 | 127.0.0.5 | 1944 Executing single-partition query on staff [SharedPool-Worker-1] | 2015-02-02 06:39:58.778000 | 127.0.0.3 | 1554 Processing response from /127.0.0.5 [SharedPool-Worker-3] | 2015-02-02 06:39:58.779000 | 127.0.0.1 | 20762 Enqueuing response to /127.0.0.1 [SharedPool-Worker-1] | 2015-02-02 06:39:58.779000 | 127.0.0.3 | 2425 Sending message to /127.0.0.5 [WRITE-/127.0.0.5] | 2015-02-02 06:39:58.779000 | 127.0.0.1 | 21198 Sending message to /127.0.0.1 [WRITE-/127.0.0.1] | 2015-02-02 06:39:58.779000 | 127.0.0.3 | 2639 Sending message to /127.0.0.6 [WRITE-/127.0.0.6] | 2015-02-02 06:39:58.779000 | 127.0.0.1 | 21208 Executing single-partition query on staff [SharedPool-Worker-1] | 2015-02-02 06:39:58.780000 | 127.0.0.5 | 304 Enqueuing response to /127.0.0.1 [SharedPool-Worker-1] | 2015-02-02 06:39:58.780001 | 127.0.0.5 | 574 Executing single-partition query on staff [SharedPool-Worker-2] | 2015-02-02 06:39:58.781000 | 127.0.0.3 | 4075 Sending message to /127.0.0.1 [WRITE-/127.0.0.1] | 2015-02-02 06:39:58.781000 | 127.0.0.5 | 708 Enqueuing response to /127.0.0.1 [SharedPool-Worker-2] | 2015-02-02 06:39:58.781001 | 127.0.0.3 | 4348 Sending message to /127.0.0.1 [WRITE-/127.0.0.1] | 2015-02-02 06:39:58.782000 | 127.0.0.3 | 5371 Executing single-partition query on staff [SharedPool-Worker-1] | 2015-02-02 06:39:58.783000 | 127.0.0.6 | 2463 Enqueuing response to /127.0.0.1 [SharedPool-Worker-1] | 2015-02-02 06:39:58.784000 | 127.0.0.6 | 2905 Sending message to /127.0.0.1 [WRITE-/127.0.0.1] | 2015-02-02 06:39:58.784001 | 127.0.0.6 | 3160 Processing response from /127.0.0.6 [SharedPool-Worker-2] | 2015-02-02 06:39:58.785000 | 127.0.0.1 | -- Request complete | 2015-02-02 06:39:58.782995 | 127.0.0.1 | 23995
  • 17. @chbatey Denormalise with UDTs CREATE TYPE store (name text, type text, postcode text) CREATE TYPE staff (name text, fav_colour text, job_title text) 
 CREATE TABLE customer_events( customer_id text, time timeuuid, event_type text, store store, staff set<staff>, PRIMARY KEY ((customer_id), time)); User defined types Cassandra 2.1 has landed in DSE 4.7!!!
  • 18. @chbatey Less obvious example • A good pattern for time series: Bucketing
  • 19. @chbatey Adding a time bucket CREATE TABLE customer_events_bucket( customer_id text, time_bucket text time timeuuid, event_type text, store store, staff set<staff>, PRIMARY KEY ((customer_id, time_bucket), time));
  • 20. @chbatey Queries select * from customer_events_bucket where customer_id = ‘chbatey’ and time_bucket IN (‘2015-01-01|0910:01’, ‘2015-01-01|0910:02’, ‘2015-01-01|0910:03’, ‘2015-01-01| 0910:04’) Often better as multiple async queries
  • 21. @chbatey Tips for avoiding joins & multi gets • Say no to client side joins by denormalising - Much easier with UDTs • When bucketing aim for at most two buckets for a query • Get in the habit of reading trace + using a multi node cluster locally
  • 23. @chbatey Unlogged Batches • Unlogged batches - Send many statements as one
  • 24. @chbatey Customer events table CREATE TABLE if NOT EXISTS customer_events ( customer_id text, statff_id text, store_type text, time timeuuid , event_type text, PRIMARY KEY (customer_id, time))
  • 25. @chbatey Inserting events INSERT INTO events.customer_events (customer_id, time , event_type , statff_id , store_type ) VALUES ( ?, ?, ?, ?, ?) public void storeEvent(ConsistencyLevel consistencyLevel, CustomerEvent customerEvent) {
 BoundStatement boundInsert = insertStatement.bind( customerEvent.getCustomerId(), customerEvent.getTime(), customerEvent.getEventType(), customerEvent.getStaffId(), customerEvent.getStaffId());
 
 boundInsert.setConsistencyLevel(consistencyLevel);
 session.execute(boundInsert);
 }
  • 26. @chbatey Batch insert - Good idea? public void storeEvents(ConsistencyLevel consistencyLevel, CustomerEvent... events) {
 BatchStatement batchStatement = new BatchStatement(BatchStatement.Type.UNLOGGED);
 
 for (CustomerEvent event : events) { BoundStatement boundInsert = insertStatement.bind( customerEvent.getCustomerId(), customerEvent.getTime(), customerEvent.getEventType(), customerEvent.getStaffId(), customerEvent.getStaffId());
 
 batchStatement.add(boundInsert); } session.execute(batchStatement); }
  • 27. @chbatey Not so much client C Very likely to fail if nodes are down / over loaded Gives far too much work for the coordinator Ruins performance gains of token aware driver
  • 29. @chbatey Unlogged batch use case public void storeEvents(String customerId, ConsistencyLevel consistencyLevel, CustomerEvent... events) {
 BatchStatement batchStatement = new BatchStatement(BatchStatement.Type.UNLOGGED);
 batchStatement.enableTracing();
 
 for (CustomerEvent event : events) {
 BoundStatement boundInsert = insertStatement.bind(
 customerId,
 event.getTime(),
 event.getEventType(),
 event.getStaffId(),
 event.getStaffId());
 boundInsert.enableTracing();
 boundInsert.setConsistencyLevel(consistencyLevel);
 batchStatement.add(boundInsert);
 }
 
 ResultSet execute = session.execute(batchStatement);
 logTraceInfo(execute.getExecutionInfo());
 } Partition Key!!
  • 30. @chbatey Writing this client C All for the same partition :)
  • 32. @chbatey Logged Batches • Once accepted the statements will eventually succeed • Achieved by writing them to a distributed batchlog • 30% slower than unlogged batches
  • 33. @chbatey Batch insert - Good idea? public void storeEvents(ConsistencyLevel consistencyLevel, CustomerEvent... events) {
 BatchStatement batchStatement = new BatchStatement(BatchStatement.Type.LOGGED);
 
 for (CustomerEvent event : events) { BoundStatement boundInsert = insertStatement.bind( customerEvent.getCustomerId(), customerEvent.getTime(), customerEvent.getEventType(), customerEvent.getStaffId(), customerEvent.getStaffId());
 
 batchStatement.add(boundInsert); } session.execute(batchStatement); }
  • 34. @chbatey Not so much client C BATCH LOG BL-R BL-R BL-R: Batch log replica
  • 35. @chbatey Use case? CREATE TABLE if NOT EXISTS customer_events ( customer_id text, statff_id text, store_type text, time timeuuid , event_type text, PRIMARY KEY (customer_id, time)) CREATE TABLE if NOT EXISTS customer_events_by_staff ( customer_id text, statff_id text, store_type text, time timeuuid , event_type text, PRIMARY KEY (staff_id, time))
  • 36. @chbatey Storing events to both tables in a batch public void storeEventLogged(ConsistencyLevel consistencyLevel, CustomerEvent customerEvent) {
 BoundStatement boundInsertForCustomerId = insertByCustomerId.bind(customerEvent.getCustomerId(), customerEvent.getTime(), customerEvent.getEventType(), customerEvent.getStaffId(), customerEvent.getStaffId());
 BoundStatement boundInsertForStaffId = insertByStaffId.bind(customerEvent.getCustomerId(), customerEvent.getTime(), customerEvent.getEventType(), customerEvent.getStaffId(), customerEvent.getStaffId()); 
 BatchStatement batchStatement = new BatchStatement(BatchStatement.Type.LOGGED);
 batchStatement.enableTracing();
 batchStatement.setConsistencyLevel(consistencyLevel);
 batchStatement.add(boundInsertForCustomerId);
 batchStatement.add(boundInsertForStaffId);
 
 
 ResultSet execute = session.execute(batchStatement);
 }
  • 38. @chbatey Distributed mutable state :-( create TABLE accounts(customer text PRIMARY KEY, balance_in_pence int); Overly naive example • Cassandra uses last write wins (no Vector clocks) • Read before write is an anti-pattern - race conditions and latency
  • 39. @chbatey Take a page from Event sourcing create table accounts_log( customer text, time timeuuid, delta int, primary KEY (customer, time)); All records for the same customer in the same storage row Transactions ordered by TIMEUUID - UUIDs are your friends in distributed systems
  • 40. @chbatey Tips • Avoid mutable state in distributed system, favour immutable log • Can roll up and snapshot to avoid calculation getting too big • If you really have to then checkout LWTs and do via CAS - this will be slower
  • 42. @chbatey Customer events table CREATE TABLE if NOT EXISTS customer_events ( customer_id text, staff_id text, store_type text, time timeuuid , event_type text, PRIMARY KEY (customer_id, time)) create INDEX on customer_events (staff_id) ;
  • 43. @chbatey Indexes to the rescue? customer_id time staff_id chbatey 2015-03-03 08:52:45 trevor chbatey 2015-03-03 08:52:54 trevor chbatey 2015-03-03 08:53:11 bill chbatey 2015-03-03 08:53:18 bill rusty 2015-03-03 08:56:57 bill rusty 2015-03-03 08:57:02 bill rusty 2015-03-03 08:57:20 trevor staff_id customer_id trevor chbatey trevor chbatey bill chbatey bill chbatey bill rusty bill rusty trevor rusty
  • 44. @chbatey Indexes to the rescue? staff_id customer_id trevor chbatey trevor chbatey bill chbatey bill chbatey staff_id customer_id bill rusty bill rusty trevor rusty A B chbatey rusty customer_id time staff_id chbatey 2015-03-03 08:52:45 trevor chbatey 2015-03-03 08:52:54 trevor chbatey 2015-03-03 08:53:11 bill chbatey 2015-03-03 08:53:18 bill rusty 2015-03-03 08:56:57 bill rusty 2015-03-03 08:57:02 bill rusty 2015-03-03 08:57:20 trevor customer_events table staff_id customer_id trevor chbatey trevor chbatey bill chbatey bill chbatey bill rusty bill rusty trevor rusty staff_id index
  • 45. @chbatey Do it your self index CREATE TABLE if NOT EXISTS customer_events ( customer_id text, statff_id text, store_type text, time timeuuid , event_type text, PRIMARY KEY (customer_id, time)) CREATE TABLE if NOT EXISTS customer_events_by_staff ( customer_id text, statff_id text, store_type text, time timeuuid , event_type text, PRIMARY KEY (staff_id, time))
  • 46. @chbatey Anti patterns summary • Most anti patterns are very obvious in trace output • Don’t test on small clusters, most problems only occur at scale
  • 47.
  • 50. @chbatey Retrying writes • Cassandra does not roll back! • R3 has the data and the coordinator has hinted for R1 and R2
  • 51. @chbatey Write timeout • Received acknowledgements • Required acknowledgements • Consistency level • CAS and Batches are more complicated: - WriteType: SIMPLE, BATCH, UNLOGGED_BATCH, BATCH_LOG, CAS
  • 52. @chbatey Batches • BATCH_LOG - Timed out waiting for batch log replicas • BATCH - Written to batch log but timed out waiting for actual replica - Will eventually be committed
  • 53. @chbatey Idempotent writes • All writes are idempotent with the following exceptions: - Counters - lists
  • 54. @chbatey Anti patterns summary • Most anti patterns are very obvious in trace output • Don’t test on small clusters, most problems only occur at scale
  • 55.
  • 56. @chbatey Cassandra as a Queue (tombstones)
  • 57. @chbatey Well documented anti-pattern • http://www.datastax.com/dev/blog/cassandra-anti- patterns-queues-and-queue-like-datasets • http://lostechies.com/ryansvihla/2014/10/20/domain- modeling-around-deletes-or-using-cassandra-as-a- queue-even-when-you-know-better/
  • 58. @chbatey Requirements • Produce data in one DC • Consume it once and delete from another DC • Use Cassandra?
  • 59. @chbatey Datamodel CREATE TABLE queues ( name text, enqueued_at timeuuid, payload blob, PRIMARY KEY (name, enqueued_at) ); SELECT enqueued_at, payload FROM queues WHERE name = 'queue-1' LIMIT 1;
  • 60. @chbatey Trace activity | source | elapsed -------------------------------------------+-----------+-------- execute_cql3_query | 127.0.0.3 | 0 Parsing statement | 127.0.0.3 | 48 Peparing statement | 127.0.0.3 | 362 Message received from /127.0.0.3 | 127.0.0.1 | 42 Sending message to /127.0.0.1 | 127.0.0.3 | 718 Executing single-partition query on queues | 127.0.0.1 | 145 Acquiring sstable references | 127.0.0.1 | 158 Merging memtable contents | 127.0.0.1 | 189 Merging data from memtables and 0 sstables | 127.0.0.1 | 235 Read 1 live and 19998 tombstoned cells | 127.0.0.1 | 251102 Enqueuing response to /127.0.0.3 | 127.0.0.1 | 252976 Sending message to /127.0.0.3 | 127.0.0.1 | 253052 Message received from /127.0.0.1 | 127.0.0.3 | 324314 Not good :(
  • 61. @chbatey Tips • Avoid lots of deletes + range queries in the same partition • Avoid it with data modelling / query pattern: - Move partition - Add a start for your range: e.g. enqueued_at > 9d1cb818-9d7a-11b6-96ba-60c5470cbf0e