Cassandra write giving very slow perfomance using Spark

Question

I have a cassandra table with around 500+ million records (In 6 nodes), now I'm trying to insert data using spark-cassandra-connector in Amazon EMR

Table Structure

  CREATE TABLE dmp.dmp_user_profiles_latest (
        pid text PRIMARY KEY,
        xnid int,
        day_count map<text, int>,
        first_seen map<text, timestamp>,
        last_seen map<text, timestamp>,
        usage_count map<text, int>,
        city text,
        country text,
        lid set<text>,

    )WITH bloom_filter_fp_chance = 0.01
    AND caching = '{"keys":"NONE", "rows_per_partition":"ALL"}'
    AND comment = ''
    AND compaction = {'min_threshold': '4', 'class': 'org.apache.cassandra.db.compaction.LeveledCompactionStrategy', 'max_threshold': '32'}
    AND compression = {'chunk_length_kb': '256', 'sstable_compression': 'org.apache.cassandra.io.compress.LZ4Compressor'}
    AND dclocal_read_repair_chance = 0.1
    AND default_time_to_live = 0
    AND gc_grace_seconds = 172800
    AND max_index_interval = 2048
    AND memtable_flush_period_in_ms = 0
    AND min_index_interval = 128
    AND read_repair_chance = 0.1
    AND speculative_retry = '99.0PERCENTILE';
CREATE INDEX dmp_user_profiles_latest_app_day_count_idx ON dmp.dmp_user_profiles_latest (day_count);
CREATE INDEX dmp_user_profiles_latest_country_idx ON dmp.dmp_user_profiles_latest (country);

The following are my spark-submit options

--class com.mobi.vserv.driver.Query5kPids1
--conf spark.dynamicAllocation.enabled=true  
--conf spark.yarn.executor.memoryOverhead=1024    
--conf spark.yarn.driver.memoryOverhead=1024 
--executor-memory 1g
--executor-cores 2
--driver-memory 4g

But in the logs I have seen writing to Cassandra takes around 4-5 minutes for loading 2 lakh (200,000) records(while total execution time is 6+ minutes)

I have added the following in Spark conf also

conf.set("spark.cassandra.output.batch.size.rows", "auto");
conf.set("spark.cassandra.output.concurrent.writes", "500");
conf.set("spark.cassandra.output.batch.size.bytes", "100000");
conf.set("spark.cassandra.output.throughput_mb_per_sec","1");

But still there is no performance improvement , also increasing the no of cores in Amazon EMR doesn't help.

Please note that In my Cassandra table we have not used any partitioning/clustering column , so could this be the reason for such slow performance.

Please Note Network speed is 30 MB PS an primary key is an Alphanumeric Values eg - a9be3eb4-751f-48ee-b593-b3f89e18622d

Cassandra.yaml

cluster_name: 'dmp Cluster'
num_tokens: 100
hinted_handoff_enabled: true
max_hint_window_in_ms: 10800000 # 3 hours
hinted_handoff_throttle_in_kb: 1024
max_hints_delivery_threads: 2
batchlog_replay_throttle_in_kb: 1024
authenticator: AllowAllAuthenticator
authorizer: AllowAllAuthorizer
permissions_validity_in_ms: 2000
partitioner: org.apache.cassandra.dht.Murmur3Partitioner
data_file_directories:
     - /data/cassandra/data
disk_failure_policy: stop
commit_failure_policy: stop

key_cache_size_in_mb:

key_cache_save_period: 14400
row_cache_size_in_mb: 0
row_cache_save_period: 0
counter_cache_size_in_mb:
counter_cache_save_period: 7200
saved_caches_directory: /data/cassandra/saved_caches
commitlog_sync: periodic
commitlog_sync_period_in_ms: 10000
seed_provider:
 - class_name: org.apache.cassandra.locator.SimpleSeedProvider
    parameters:
 - seeds: "10.142.76.97,10.182.19.301"

concurrent_reads: 256
concurrent_writes: 128
concurrent_counter_writes: 32

memtable_allocation_type: heap_buffers
memtable_flush_writers: 8
index_summary_capacity_in_mb:
index_summary_resize_interval_in_minutes: 60
trickle_fsync: false
trickle_fsync_interval_in_kb: 10240
storage_port: 7000
ssl_storage_port: 7001
listen_address: 10.142.76.97
start_rpc: true
rpc_address: 10.23.244.172
rpc_port: 9160
rpc_keepalive: true
rpc_server_type: sync
thrift_framed_transport_size_in_mb: 15
incremental_backups: false
snapshot_before_compaction: false
auto_snapshot: true
tombstone_warn_threshold: 1000
tombstone_failure_threshold: 100000
column_index_size_in_kb: 64
batch_size_warn_threshold_in_kb: 5
concurrent_compactors: 4
compaction_throughput_mb_per_sec: 64
sstable_preemptive_open_interval_in_mb: 50
read_request_timeout_in_ms: 500000

range_request_timeout_in_ms: 1000000

write_request_timeout_in_ms: 200000

counter_write_request_timeout_in_ms: 500000

cas_contention_timeout_in_ms: 100000

endpoint_snitch: Ec2Snitch

dynamic_snitch_update_interval_in_ms: 100

dynamic_snitch_reset_interval_in_ms: 600000

dynamic_snitch_badness_threshold: 0.1

request_scheduler: org.apache.cassandra.scheduler.NoScheduler

server_encryption_options:
    internode_encryption: none
    keystore: conf/.keystore
    keystore_password: cassandra
    truststore: conf/.truststore
    truststore_password: cassandra

client_encryption_options:
    enabled: false
    keystore: conf/.keystore
    keystore_password: cassandra

internode_compression: all

inter_dc_tcp_nodelay: false

Answer 1

As talked in the comment, it seems your problem comes from your index on day_count.

As seen in this page, index won't be efficient if you must update them all the time, and it does when you insert a different value into day_count (which is possibly everytime).

You need to rework your database, but as this is your production environment, you can't just DROP INDEX IF EXISTS keyspace.index_name if this index is necessary, but you could create a secondary database using day_count as the primary key, or use day_count as an ordering index.