ErlCass

An Erlang Cassandra driver, based on DataStax cpp driver focused on performance.

Update from 2.x to 3.0

This update breaks the compatibility with the other versions. All query results will return in case of success:

ok instead {ok, []} for all DDL and DML queries (because they never returns any column or row)
{ok, Columns, Rows} instead {ok, Rows}, where also each row is returned as a list not as a tuple as was before.

Implementation note

How ErlCass affects the Erlang schedulers

It's well known that NIF's can affect the Erlang schedulers performances in case the functions are not returning in less than 1-2 ms and blocks the threads.

Because the DataStax cpp driver is async, ErlCass won't block the scheduler threads and all calls to the native functions will return immediately. The DataStax driver use it's own thread pool for managing the requests. Also the responses are received on this threads and sent back to Erlang calling processes using enif_send in an async manner.

Benchmark comparing with other drivers

The benchmark (benchmarks/benchmark.erl) is spawning N processes that will send a total of X request using the async api's and then waits to read X responses. In benchmarks/benchmark.config you can find the config's for every driver used in tests. During test in case of unexpected results from driver will log errors in console.

To run the benchmark yourself you should do:

in rebar.config remove comment for cqerl and marina deps
copy benchmarks/benchmark.erl and load_test.erl in src
recompile using rebar3
change the cluster ip in benchmark.config for all drivers
create the testing keyspace and tables using load_test:prepare_load_test_table().
use make benchmark as described above

The following test was run on a MacBook Pro with Mac OS Sierra 10.12.6 and the cassandra cluster was running on other 3 physical machines in the same LAN. The schema was created using load_test:prepare_load_test_table from benchmarks/load_test.erl. Basically the schema contains all possible data types and the query is based on a primary key (will return the same row all the time which is fine because we test the driver performances and not the server one)

make benchmark MODULE=erlcass PROCS=100 REQ=100000

Where:

MODULE: the driver used to benchmark. Can be one of : erlcass, cqerl or marina
PROCS: the number or erlang processes used to send the requests (concurrency level). Default 100.
REQ: the number of requests to be sent. Default 100000.

The results for 100 concurrent processes that sends 100k queries. Measured the average time for 3 runs:

cassandra driver	Time (ms)	Req/sec
erlcass v3.0	1466	68212
cqerl v1.0.8	11016	9077
marina 0.2.17	1779	56221

Notes:

marina performs very nice unfortunately you need to tune properly the backlog_size and pool_size based on the concurrency level you are using. From my test performance degrades a lot if pool size is increased (for example for 100 connections time to complete was 3044 ms instead 1779 ms for 30 connections) Also in case te pool is too small you start getting all kind of errors (like no socket available) or in case the backlog is not big enough you get errors as well.
erlcass seems to have the smallest variation between tests. Results are always in the same range +/- 100 ms. On the other drivers might happened time to time to have bigger variations.

Changelog

Changelog is available here.

Getting started:

The application is compatible with both rebar or rebar3.

In case you receive any error related to compiling of the DataStax driver you can try to run rebar with sudo in order to install all dependencies. Also you can check wiki section for more details

Data types

In order to see the relation between Cassandra column types and Erlang types please check this wiki section

Starting the application

application:start(erlcass).

Setting the log level

Erlcass is using lager for logging the errors. Beside the fact that you can set in lager the desired log level, for better performances it's better to set also in erlcass the desired level otherwise there will be a lot of resources consumed by lager to format the messages and then drop them. Also the native driver performances can be affected because of the time spent in generating the logs and sending them from C++ into Erlang.

Available Log levels are:

-define(CASS_LOG_DISABLED, 0).
-define(CASS_LOG_CRITICAL, 1).
-define(CASS_LOG_ERROR, 2).
-define(CASS_LOG_WARN, 3). (default)
-define(CASS_LOG_INFO, 4).
-define(CASS_LOG_DEBUG,5).
-define(CASS_LOG_TRACE, 6).

In order to change the log level for the native driver you need to set the log_level environment variable for erlcass into your app config file, example: {log_level, 3}.

Setting the cluster options

The cluster options can be set inside your app.config file under the cluster_options key:

{erlcass, [
    {log_level, 3},
    {keyspace, <<"keyspace">>},
    {cluster_options,[
        {contact_points, <<"172.17.3.129,172.17.3.130,172.17.3.131">>},
        {port, 9042},
        {load_balance_dc_aware, {<<"dc-name">>, 0, false}},
        {latency_aware_routing, true},
        {token_aware_routing, true},
        {number_threads_io, 4},
        {queue_size_io, 128000},
        {max_connections_host, 5},
        {pending_requests_high_watermark, 128000},
        {tcp_nodelay, true},
        {tcp_keepalive, {true, 1800}},
        {default_consistency_level, 6}
    ]}
]},

Tips for production environment:

Use token_aware_routing and latency_aware_routing
Don't use number_threads_io bigger than the number of your cores.
Use tcp_nodelay and also enable tcp_keepalive

All available options are described in the following wiki section.

Add a prepare statement

Example:

ok = erlcass:add_prepare_statement(select_blogpost,
                                   <<"select * from blogposts where domain = ? LIMIT 1">>),

In case you want to overwrite the default consistency level for that prepare statement use a tuple for the query argument: {Query, ConsistencyLevelHere}

Also this is possible using {Query, Options} where options is a proplist with the following options supported:

consistency_level - If it's missing the statement will be executed using the default consistency level value.
serial_consistency_level - This consistency can only be either ?CASS_CONSISTENCY_SERIAL or ?CASS_CONSISTENCY_LOCAL_SERIAL and if not present, it defaults to ?CASS_CONSISTENCY_SERIAL. This option will be ignored for anything else that a conditional update/insert.

Example:

ok = erlcass:add_prepare_statement(select_blogpost,
        {<<"select * from blogposts where domain = ? LIMIT 1">>, ?CASS_CONSISTENCY_LOCAL_QUORUM}).

ok = erlcass:add_prepare_statement(insert_blogpost, {
        <<"UPDATE blogposts SET author = ? WHERE domain = ? IF EXISTS">>, [
        {consistency_level, ?CASS_CONSISTENCY_LOCAL_QUORUM},
        {serial_consistency_level, ?CASS_CONSISTENCY_LOCAL_SERIAL}]
}).

Run a prepared statement query

You can bind the parameters in 2 ways: by name and by index. You can use ?BIND_BY_INDEX and ?BIND_BY_NAME from execute/3 in order to specify the desired method. By default is binding by index

Example:

%bind by name
erlcass:execute(select_blogpost, ?BIND_BY_NAME, [{<<"domain">>, <<"Domain_1">>}]).

%bind by index
erlcass:execute(select_blogpost, [<<"Domain_1">>]).

%bind by index
erlcass:execute(select_blogpost, ?BIND_BY_INDEX, [<<"Domain_1">>]).

In case of maps you can use key(field) and value(field) in order to bind by name.

%table: CREATE TABLE test_map(key int PRIMARY KEY, value map<text,text>)
%statement: UPDATE examples.test_map SET value[?] = ? WHERE key = ?

%bind by index

erlcass:execute(identifier, [<<"collection_key_here">>, <<"collection_value_here">>, <<"key_here">>]).

%bind by name

erlcass:execute(insert_test_bind, ?BIND_BY_NAME, [
    {<<"key(value)">>, CollectionIndex1}, 
    {<<"value(value)">>, CollectionValue1}, 
    {<<"key">>, Key1}
]),

Async queries and blocking queries

For blocking operations use erlcass:execute, for async execution use : erlcass:async_execute.

The blocking operation the calling process will block (still async into the native code in order to avoid freezing of the VM threads) until will get the result from the cluster.

In case of an async execution the calling process will receive a message of the following format: {execute_statement_result, Tag, Result} when the data from the server was retrieved.

For example:

{ok, Tag} = erlcass:async_execute(...),
    receive
        {execute_statement_result, Tag, Result} ->
            Result
    end.

Non prepared statements queries

In order to run queries that you don't want to run them as prepared statements you can use: query/1, query_async/1 or query_new_statement/1 (in order to create a query statement that can be executed into a batch query along other prepared or not prepared statements)

The same rules apply for setting the desired consistency level as on prepared statements (see Add prepare statement section).

erlcass:query(<<"select * from blogposts where domain = 'Domain_1' LIMIT 1">>).

Batched queries

In order to perform batched statements you can use erlcass:batch_async_execute/3 or erlcass:batch_execute/3.

First argument is the batch type and is defined as:

-define(CASS_BATCH_TYPE_LOGGED, 0).
-define(CASS_BATCH_TYPE_UNLOGGED, 1).
-define(CASS_BATCH_TYPE_COUNTER, 2).

The second one is a list of statements (prepared or normal statements) that needs to be executed in the batch.

The third argument is a list of options in {Key, Value} format (proplist):

consistency_level - If it's missing the batch will be executed using the default consistency level value.
serial_consistency_level - That consistency can only be either ?CASS_CONSISTENCY_SERIAL or ?CASS_CONSISTENCY_LOCAL_SERIAL and if not present, it defaults to ?CASS_CONSISTENCY_SERIAL. This option will be ignored for anything else that a conditional update/insert.

Example:

ok = erlcass:add_prepare_statement(insert_prep, <<"INSERT INTO table1(id, age, email) VALUES (?, ?, ?)">>),

{ok, Stm1} = erlcass:query_new_statement(<<"UPDATE table2 set foo = 'bar'">>),

{ok, Stm2} = erlcass:bind_prepared_statement(insert_prep),
ok = erlcass:bind_prepared_params_by_index(Stm2, [Id2, Age2, Email2]),

ok = erlcass:batch_execute(?CASS_BATCH_TYPE_LOGGED, [Stm1, Stm2], [
    {consistency_level, ?CASS_CONSISTENCY_QUORUM}
]).

Working with uuid or timeuuid fields:

erlcass_uuid:gen_time() -> Generates a V1 (time) UUID
erlcass_uuid:gen_random() -> Generates a new V4 (random) UUID
erlcass_uuid:gen_from_ts(Ts) -> Generates a V1 (time) UUID for the specified timestamp
erlcass_uuid:min_from_ts(Ts) -> Sets the UUID to the minimum V1 (time) value for the specified timestamp,
erlcass_uuid:max_from_ts(Ts) -> Sets the UUID to the maximum V1 (time) value for the specified timestamp,
erlcass_uuid:get_ts(Uuid) -> Gets the timestamp for a V1 UUID,
erlcass_uuid:get_version(Uuid) -> Gets the version for a UUID (V1 or V4)

Working with date, time fields:

erlcass_time:date_from_epoch(EpochSecs) -> Converts a unix timestamp (in seconds) to the Cassandra date type. The date type represents the number of days since the Epoch (1970-01-01) with the Epoch centered at the value 2^31.
erlcass_time:time_from_epoch(EpochSecs) -> Converts a unix timestamp (in seconds) to the Cassandra time type. The time type represents the number of nanoseconds since midnight (range 0 to 86399999999999).
erlcass_time:date_time_to_epoch(Date, Time) -> Combines the Cassandra date and time types to Epoch time in seconds. Returns Epoch time in seconds. Negative times are possible if the date occurs before the Epoch (1970-1-1).

Getting metrics

In order to get metrics from the native driver you can use erlcass:get_metrics().

requests

min - Minimum in microseconds
max - Maximum in microseconds
mean - Mean in microseconds
stddev - Standard deviation in microseconds
median - Median in microseconds
percentile_75th - 75th percentile in microseconds
percentile_95th - 95th percentile in microseconds
percentile_98th - 98th percentile in microseconds
percentile_99th - 99the percentile in microseconds
percentile_999th - 99.9th percentile in microseconds
mean_rate - Mean rate in requests per second
one_minute_rate - 1 minute rate in requests per second
five_minute_rate - 5 minute rate in requests per second
fifteen_minute_rate - 15 minute rate in requests per second

stats

total_connections - The total number of connections
available_connections - The number of connections available to take requests
exceeded_pending_requests_water_mark - Occurrences when requests exceeded a pool's water mark
exceeded_write_bytes_water_mark - Occurrences when number of bytes exceeded a connection's water mark

errors

connection_timeouts - Occurrences of a connection timeout
pending_request_timeouts - Occurrences of requests that timed out waiting for a connection
request_timeouts - Occurrences of requests that timed out waiting for a request to finish

Low level methods

Each query requires an internal statement (prepared or not). You can reuse the same statement object for multiple queries performed in the same process.

Getting a statement reference for a prepared statement query

{ok, Statement} = erlcass:bind_prepared_statement(select_blogpost).

Getting a statement reference for a non prepared query

{ok, Statement} = erlcass:query_new_statement(<<"select * from blogposts where domain = 'Domain_1' LIMIT 1">>).

Bind the values for a prepared statement before executing

%bind by name
ok = erlcass:bind_prepared_params_by_name(select_blogpost, [{<<"domain">>, <<"Domain_1">>}]);

%bind by index
ok = erlcass:bind_prepared_params_by_index(select_blogpost, [<<"Domain_1">>]);

For mode details about bind by index and name please see: 'Run a prepared statement query' section

blaketan/erlcass

ErlCass

Update from 2.x to 3.0

Implementation note

How ErlCass affects the Erlang schedulers

Benchmark comparing with other drivers

Changelog

Getting started:

Data types

Starting the application

Setting the log level

Setting the cluster options

Add a prepare statement

Run a prepared statement query

Async queries and blocking queries

Non prepared statements queries

Batched queries

Working with uuid or timeuuid fields:

Working with date, time fields:

Getting metrics

requests

stats

errors

Low level methods

Getting a statement reference for a prepared statement query

Getting a statement reference for a non prepared query

Bind the values for a prepared statement before executing