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Webinar Cassandra Anti-Patterns

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Christopher Batey
Christopher Batey

Webinar Cassandra Anti-Patterns

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©2013 DataStax Confidential. Do not distribute without consent. @chbatey Christopher Batey
 Technical Evangelist for Apache Cassandra Avoiding anti-patterns: Staying in love with Cassandra
@chbatey Who am I? • Technical Evangelist for Apache Cassandra •Work on Stubbed Cassandra •Help out Apache Cassandra users • Built systems using Java/Spring/Dropwizard with Cassandra @ Sky • Follow me on twitter @chbatey
@chbatey Anti patterns • Client side joins • Multi partition queries • Batches • Mutable data • Retry policies • Tombstones • Secondary indices • Includes home work + prize
@chbatey Distributed joins
@chbatey Cassandra can not join or aggregate 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
@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 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 • UNLOGGED_BATCH
@chbatey Idempotent writes • All writes are idempotent with the following exceptions: - Counters - lists
@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 • 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
@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 Inserts INSERT INTO customer_events (customer_id, time , event_type , staff_id, store_type ) VALUES ( 'rusty', 42730bf0-c183-11e4-a4c6-1971740a12cd, 'SELL', 'trevor', 'WEB'); INSERT INTO customer_events (customer_id, time , event_type , staff_id, store_type ) VALUES ( 'rusty', 3f07cd70-c183-11e4-a4c6-1971740a12cd, 'BUY', 'trevor', 'WEB'); INSERT INTO customer_events (customer_id, time , event_type , staff_id, store_type ) VALUES ( 'chbatey', a9550c70-c182-11e4-a4c6-1971740a12cd, 'BUY', 'trevor', 'WEB'); INSERT INTO customer_events (customer_id, time , event_type , staff_id, store_type ) VALUES ( 'chbatey', aebbf3e0-c182-11e4-a4c6-1971740a12cd, 'SELL', 'trevor', 'WEB'); INSERT INTO customer_events (customer_id, time , event_type , staff_id, store_type ) VALUES ( 'chbatey', b8c2f050-c182-11e4-a4c6-1971740a12cd, 'VIEW', 'bill', 'WEB'); INSERT INTO customer_events (customer_id, time , event_type , staff_id, store_type ) VALUES ( 'chbatey', bcb5ae50-c182-11e4-a4c6-1971740a12cd, 'BUY', 'bill', 'WEB');
@chbatey Secondary index are local • The staff_id partition in the secondary index is not distributed like a normal table • The secondary index entries are only stored on the node that contains the customer_id partition
@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 Homework 1. Use a cluster with 6 nodes (CCM) 2. Create the customer events table and add the secondary index 3. Insert the data (insert statements are in a hidden slide I’ll distribute) 4. Turn tracing on and execute the following queries: A. select * from customer_events where staff_id = 'trevor' ; B. select * from customer_events where staff_id = 'trevor' and customer_id = ‘chbatey’ 5. How many partitions queried and nodes were used for each query? 6. Send me the answer on twitter, first couple get SWAG!
@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 Possibly in 3.0 - Global indexes
@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 Thanks for listening • Check out my blog: http://christopher- batey.blogspot.co.uk/ - Answers for all questions - Detailed posts on most of the anti patterns + full working code • More questions? Contact me on twitter: @chbatey • Learn more about read / write path, CCM checkout DataStax academy - https://academy.datastax.com/
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Webinar Cassandra Anti-Patterns

  • 1. ©2013 DataStax Confidential. Do not distribute without consent. @chbatey Christopher Batey
 Technical Evangelist for Apache Cassandra Avoiding anti-patterns: Staying in love with Cassandra
  • 2. @chbatey Who am I? • Technical Evangelist for Apache Cassandra •Work on Stubbed Cassandra •Help out Apache Cassandra users • Built systems using Java/Spring/Dropwizard with Cassandra @ Sky • Follow me on twitter @chbatey
  • 3. @chbatey Anti patterns • Client side joins • Multi partition queries • Batches • Mutable data • Retry policies • Tombstones • Secondary indices • Includes home work + prize
  • 5. @chbatey Cassandra can not join or aggregate Client Where do I go for the max?
  • 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
  • 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
  • 43. @chbatey Retrying writes • Cassandra does not roll back! • R3 has the data and the coordinator has hinted for R1 and R2
  • 44. @chbatey Write timeout • Received acknowledgements • Required acknowledgements • Consistency level • CAS and Batches are more complicated: - WriteType: SIMPLE, BATCH, UNLOGGED_BATCH, BATCH_LOG, CAS
  • 45. @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 • UNLOGGED_BATCH
  • 46. @chbatey Idempotent writes • All writes are idempotent with the following exceptions: - Counters - lists
  • 47. @chbatey Cassandra as a Queue (tombstones)
  • 48. @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/
  • 49. @chbatey Requirements • Produce data in one DC • Consume it once and delete from another DC • Use Cassandra?
  • 50. @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;
  • 51. @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 :(
  • 52. @chbatey Tips • 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
  • 54. @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) ;
  • 55. @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
  • 56. @chbatey Inserts INSERT INTO customer_events (customer_id, time , event_type , staff_id, store_type ) VALUES ( 'rusty', 42730bf0-c183-11e4-a4c6-1971740a12cd, 'SELL', 'trevor', 'WEB'); INSERT INTO customer_events (customer_id, time , event_type , staff_id, store_type ) VALUES ( 'rusty', 3f07cd70-c183-11e4-a4c6-1971740a12cd, 'BUY', 'trevor', 'WEB'); INSERT INTO customer_events (customer_id, time , event_type , staff_id, store_type ) VALUES ( 'chbatey', a9550c70-c182-11e4-a4c6-1971740a12cd, 'BUY', 'trevor', 'WEB'); INSERT INTO customer_events (customer_id, time , event_type , staff_id, store_type ) VALUES ( 'chbatey', aebbf3e0-c182-11e4-a4c6-1971740a12cd, 'SELL', 'trevor', 'WEB'); INSERT INTO customer_events (customer_id, time , event_type , staff_id, store_type ) VALUES ( 'chbatey', b8c2f050-c182-11e4-a4c6-1971740a12cd, 'VIEW', 'bill', 'WEB'); INSERT INTO customer_events (customer_id, time , event_type , staff_id, store_type ) VALUES ( 'chbatey', bcb5ae50-c182-11e4-a4c6-1971740a12cd, 'BUY', 'bill', 'WEB');
  • 57. @chbatey Secondary index are local • The staff_id partition in the secondary index is not distributed like a normal table • The secondary index entries are only stored on the node that contains the customer_id partition
  • 58. @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
  • 59. @chbatey Homework 1. Use a cluster with 6 nodes (CCM) 2. Create the customer events table and add the secondary index 3. Insert the data (insert statements are in a hidden slide I’ll distribute) 4. Turn tracing on and execute the following queries: A. select * from customer_events where staff_id = 'trevor' ; B. select * from customer_events where staff_id = 'trevor' and customer_id = ‘chbatey’ 5. How many partitions queried and nodes were used for each query? 6. Send me the answer on twitter, first couple get SWAG!
  • 60. @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))
  • 61. @chbatey Possibly in 3.0 - Global indexes
  • 62. @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
  • 63. @chbatey Thanks for listening • Check out my blog: http://christopher- batey.blogspot.co.uk/ - Answers for all questions - Detailed posts on most of the anti patterns + full working code • More questions? Contact me on twitter: @chbatey • Learn more about read / write path, CCM checkout DataStax academy - https://academy.datastax.com/