ErlCass

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

Note for v4.0.0

• Starting with erlcass version v4.x the native driver is based on Datastax cpp-driver > 2.10.0 which is a massive release that includes many new features as well as architectural and performance improvements.

• Some cluster configs were removed while other configs were added. For more info please see the Changelog.

• This new version adds support for speculative execution: For certain applications it is of the utmost importance to minimize latency. Speculative execution is a way to minimize latency by preemptively executing several instances of the same query against different nodes. The fastest response is then returned to the client application, and the other requests are cancelled. Speculative execution is disabled by default. (see speculative_execution_policy)

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 its own thread pool for managing the requests. Also, the responses are received on these threads and sent back to Erlang calling processes using enif_send in an async manner.

Features

List of supported features:

• Asynchronous API
• Synchronous API
• Simple, Prepared, and Batch statements
• Paged queries
• Asynchronous I/O, parallel execution, and request pipelining
• Connection pooling
• Automatic node discovery
• Automatic reconnection
• Configurable load balancing
• Works with any cluster size
• Authentication
• SSL
• Latency-aware routing
• Performance metrics
• Tuples and UDTs
• Nested collections
• Retry policies
• Support for materialized view and secondary index metadata
• Support for clustering key order, frozen<> and Cassandra version metadata
• Reverse DNS with SSL peer identity verification support
• Randomized contact points
• Speculative execution

Missing features from Datastax driver can be found into the Todo List.

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:

• change the cluster ip in benchmark.config for all drivers
• run make setup_benchmark (this will compile the app using the bench profile and create the necessary schema)
• use make benchmark as described above

The following test was run on a Ubuntu 16.04 LTS (Intel(R) Core(TM) i5-2500 CPU @ 3.30GHz 4 cores) and the cassandra cluster was running on other 3 physical machines in the same LAN. The schema is created using 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)

To create schema:

make setup_benchmark

To run the benchmark:

make benchmark MODULE=erlcass PROCS=100 REQ=100000

Where:

• MODULE: the driver used to benchmark. Can be one of : erlcass 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. Picked the average time from 3 runs:

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">>},       
        {latency_aware_routing, true},
        {token_aware_routing, true},
        {number_threads_io, 4},
        {queue_size_io, 128000},
        {core_connections_host, 1},
        {tcp_nodelay, true},
        {tcp_keepalive, {true, 60}},
        {connect_timeout, 5000},
        {request_timeout, 5000},
        {retry_policy, {default, true}},
        {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 enable tcp_keepalive
• Don't use large values for core_connections_host. The driver is system call bound and performs better with less I/O threads and connections because it can batch a larger number of writes into a single system call (the driver will naturally attempt to coalesce these operations). You may want to reduce the number of I/O threads to 2 or 3 and reduce the core connections to 1 (default).

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}).

or

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}
]).

Paged queries

In order to perform paged query statements you can use erlcass:async_execute_paged/2, erlcass:async_execute_paged/3 or erlcass:execute_paged/2.

Statement paging is set with erlcass:set_paging_size/2.

Example:

ok = erlcass:add_prepare_statement(paged_query_prep, <<"SELECT val FROM table1">>),
{ok, Stm} = erlcass:bind_prepared_statement(paged_query_prep),
PageSize = 3,
ok = erlcass:set_paging_size(Stm, PageSize),
{ok, Columns, Rows1, HasMore1} = erlcass:execute_paged(Stm, paged_query_prep),
% Continue get more rows from same Stm until HasMore is false
% In this example, Rows1 contains at most 3 rows [[val1], [val2], [val3]]
%{ok, Columns, Rows2, HasMore2} = erlcass:execute_paged(Stm, paged_query_prep),

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

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