#cassandra #workload #benchmarking #performance #apache #error

app latte-cli

A database benchmarking tool for Apache Cassandra

5 releases (breaking)

0.25.0 Feb 24, 2024
0.23.0 Mar 14, 2023
0.22.0 Feb 14, 2023
0.21.0 Feb 3, 2023
0.19.0 Jan 30, 2023

#309 in Database interfaces

49 downloads per month

Apache-2.0

6.5MB
3.5K SLoC

Lightweight Benchmarking Tool for Apache Cassandra

Runs custom CQL workloads against a Cassandra cluster and measures throughput and response times

Why Yet Another Benchmarking Program?

  • Latte outperforms other benchmarking tools for Apache Cassandra by a wide margin. See benchmarks.
  • Latte aims to offer the most flexible way of defining workloads.

Performance

Contrary to NoSQLBench, Cassandra Stress and tlp-stress, Latte has been written in Rust and uses the native Cassandra driver from Scylla. It features a fully asynchronous, thread-per-core execution engine, capable of running thousands of requests per second from a single thread.

Latte has the following unique performance characteristics:

  • Great scalability on multi-core machines.
  • About 10x better CPU efficiency than NoSQLBench. This means you can test large clusters with a small number of clients.
  • About 50x-100x lower memory footprint than Java-based tools.
  • Very low impact on operating system resources – low number of syscalls, context switches and page faults.
  • No client code warmup needed. The client code works with maximum performance from the first benchmark cycle. Even runs as short as 30 seconds give accurate results.
  • No GC pauses nor HotSpot recompilation happening in the middle of the test. You want to measure hiccups of the server, not the benchmarking tool.

The excellent performance makes it a perfect tool for exploratory benchmarking, when you quickly want to experiment with different workloads.

Flexibility

Other benchmarking tools often use configuration files to specify workload recipes. Although that makes it easy to define simple workloads, it quickly becomes cumbersome when you want to script more realistic scenarios that issue multiple queries or need to generate data in different ways than the ones directly built into the tool.

Instead of trying to bend a popular configuration file format into a turing-complete scripting language, Latte simply embeds a real, fully-featured, turing-complete, modern scripting language. We chose Rune due to painless integration with Rust, first-class async support, satisfying performance and great support from its maintainers.

Rune offers syntax and features similar to Rust, albeit with dynamic typing and easy automatic memory management. Hence, you can not only just issue custom CQL queries, but you can program
anything you wish. There are variables, conditional statements, loops, pattern matching, functions, lambdas, user-defined data structures, objects, enums, constants, macros and many more.

Features

  • Compatible with Apache Cassandra 3.x, 4.x, DataStax Enterprise 6.x and ScyllaDB
  • Custom workloads with a powerful scripting engine
  • Asynchronous queries
  • Prepared queries
  • Programmable data generation
  • Workload parameterization
  • Accurate measurement of throughput and response times with error margins
  • No coordinated omission
  • Configurable number of connections and threads
  • Rate and concurrency limiters
  • Progress bars
  • Beautiful text reports
  • Can dump report in JSON
  • Side-by-side comparison of two runs
  • Statistical significance analysis of differences corrected for auto-correlation

Limitations

Latte is still early stage software under intensive development.

  • Binding some CQL data types is not yet supported, e.g. user defined types, maps or integer types smaller than 64-bit.
  • Query result sets are not exposed yet.
  • The set of data generating functions is tiny and will be extended soon.
  • Backwards compatibility may be broken frequently.

Installation

From deb package

dpkg -i latte-<version>.deb

From source

  1. Install Rust toolchain
  2. Run cargo install latte-cli

Usage

Start a Cassandra cluster somewhere (can be a local node). Then run:

latte schema <workload.rn> [<node address>] # create the database schema 
latte load <workload.rn> [<node address>]   # populate the database with data
latte run <workload.rn> [-f <function>] [<node address>]  # execute the workload and measure the performance 

You can find a few example workload files in the workloads folder. For convenience, you can place workload files under /usr/share/latte/workloads or .local/share/latte/workloads, so latte can find them regardless of the current working directory. You can also set up custom workload locations by setting LATTE_WORKLOAD_PATH environment variable.

Latte produces text reports on stdout but also saves all data to a json file in the working directory. The name of the file is created automatically from the parameters of the run and a timestamp.

You can display the results of a previous run with latte show:

latte show <report.json>
latte show <report.json> -b <previous report.json>  # to compare against baseline performance

Run latte --help to display help with the available options.

Workloads

Workloads for Latte are fully customizable with embedded scripting language Rune.

A workload script defines a set of public functions that Latte calls automatically. A minimum viable workload script must define at least a single public async function run with two arguments:

  • ctx – session context that provides the access to Cassandra
  • i – current unique cycle number of a 64-bit integer type, starting at 0

The following script would benchmark querying the system.local table:

pub async fn run(ctx, i) {
  ctx.execute("SELECT cluster_name FROM system.local LIMIT 1").await
}

Instance functions on ctx are asynchronous, so you should call await on them.

The workload script can provide more than one function for running the benchmark. In this case you can name those functions whatever you like, and then select one of them with -f / --function parameter.

Schema creation

You can (re)create your own keyspaces and tables needed by the benchmark in the schema function. The schema function should also drop the old schema if present. The schema function is executed by running latte schema command.

pub async fn schema(ctx) {
  ctx.execute("CREATE KEYSPACE IF NOT EXISTS test \
                 WITH REPLICATION = { 'class' : 'SimpleStrategy', 'replication_factor' : 1 }").await?;
  ctx.execute("DROP TABLE IF NOT EXISTS test.test").await?;
  ctx.execute("CREATE TABLE test.test(id bigint, data varchar)").await?;
}

Prepared statements

Calling ctx.execute is not optimal, because it doesn't use prepared statements. You can prepare statements and register them on the context object in the prepare function:

const INSERT = "my_insert";
const SELECT = "my_select";

pub async fn prepare(ctx) {
  ctx.prepare(INSERT, "INSERT INTO test.test(id, data) VALUES (?, ?)").await?;
  ctx.prepare(SELECT, "SELECT * FROM test.test WHERE id = ?").await?;
}

pub async fn run(ctx, i) {
  ctx.execute_prepared(SELECT, [i]).await
}

Populating the database

Read queries are more interesting when they return non-empty result sets.

To be able to load data into tables with latte load, you need to set the number of load cycles on the context object and define the load function:

pub async fn prepare(ctx) {
  ctx.load_cycle_count = 1000000;
}

pub async fn load(ctx, i) {
  ctx.execute_prepared(INSERT, [i, "Lorem ipsum dolor sit amet"]).await
}

We also recommend defining the erase function to erase the data before loading so that you always get the same dataset regardless of the data that were present in the database before:

pub async fn erase(ctx) {
  ctx.execute("TRUNCATE TABLE test.test").await
}

Generating data

Latte comes with a library of data generating functions. They are accessible in the latte crate. Typically, those functions accept an integer i cycle number, so you can generate consistent numbers. The data generating functions are pure, i.e. invoking them multiple times with the same parameters yields always the same results.

  • latte::uuid(i) – generates a random (type 4) UUID
  • latte::hash(i) – generates a non-negative integer hash value
  • latte::hash2(a, b) – generates a non-negative integer hash value of two integers
  • latte::hash_range(i, max) – generates an integer value in range 0..max
  • latte::hash_select(i, vector) – selects an item from a vector based on a hash
  • latte::blob(i, len) – generates a random binary blob of length len
  • latte::normal(i, mean, std_dev) – generates a floating point number from a normal distribution

Numeric conversions

Rune represents integers as 64-bit signed values. Therefore, it is possible to directly pass a Rune integer to a Cassandra column of type bigint. However, binding a 64-bit value to smaller integer column types, like int, smallint or tinyint will result in a runtime error. As long as an integer value does not exceed the bounds, you can convert it to smaller signed integer types by using the following instance functions:

  • x.to_i32() – converts a float or integer to a 32-bit signed integer, compatible with Cassandra int type
  • x.to_i16() – converts a float or integer to a 16-bit signed integer, compatible with Cassandra smallint type
  • x.to_i8() – converts a float or integer to an 8-bit signed integer, compatible with Cassandra tinyint type
  • x.clamp(min, max) – restricts the range of an integer or a float value to given range

You can also convert between floats and integers by calling to_integer or to_float instance functions.

Text resources

Text data can be loaded from files or resources with functions in the fs module:

  • fs::read_to_string(file_path) – returns file contents as a string
  • fs::read_lines(file_path) – reads file lines into a vector of strings
  • fs::read_resource_to_string(resource_name) – returns builtin resource contents as a string
  • fs::read_resource_lines(resource_name) – returns builtin resource lines as a vector of strings

The resources are embedded in the program binary. You can find them under resources folder in the source tree.

To reduce the cost of memory allocation, it is best to load resources in the prepare function only once and store them in the data field of the context for future use in load and run:

pub async fn prepare(ctx) {
  ctx.data.last_names = fs::read_lines("lastnames.txt")?;
  // ... prepare queries
}

pub async fn run(ctx, i) {
  let random_last_name = latte::hash_select(i, ctx.data.last_names);
  // ... use random_last_name in queries
}

Parameterizing workloads

Workloads can be parameterized by parameters given from the command line invocation. Use latte::param!(param_name, default_value) macro to initialize script constants from command line parameters:

const ROW_COUNT = latte::param!("row_count", 1000000);

pub async fn prepare(ctx) {
  ctx.load_cycle_count = ROW_COUNT;
} 

Then you can set the parameter by using -P:

latte run <workload> -P row_count=200

Error handling

Errors during execution of a workload script are divided into three classes:

  • compile errors – the errors detected at the load time of the script; e.g. syntax errors or referencing an undefined variable. These are signalled immediately and terminate the benchmark even before connecting to the database.
  • runtime errors / panics – e.g. division by zero or array out of bounds access. They terminate the benchmark immediately.
  • error return values – e.g. when the query execution returns an error result. Those take effect only when actually returned from the function (use ? for propagating them up the call chain). All errors except Cassandra overload errors terminate
    the benchmark immediately. Overload errors (e.g. timeouts) that happen during the main run phase are counted and reported in the benchmark report.

Dependencies

~57MB
~814K SLoC