TxnSails

The code base of TxnSails: Achieving Serializable Transaction Scheduling with Self-Adaptive Isolation Level Selection.

Brief introduction

TxnSails works in the middle tier between database and application. It meets three requirements:

1. It requires minimal modifications to client applications and database kernels, ensuring low implementation overhead.
2. It must be efficient to handle dangerous structures under various lower isolation levels while ensuring SER.
3. It must adaptively select the optimal isolation level to maximize performance in response to dynamic workloads.

The architecture of TxnSails is illustrated in figure below.

Code description

Code Navigation

Key modules and corresponding source code:

1. Analyzer - src/.../worker/OfflineWorker, src/.../analysis/*
2. Executor - src/.../worker/OnlineWorker, src/.../execution/validation/*
3. Adapter - src/.../worker/{Adapter, Flusher}, src/.../execution/sample/*, isolation_adapter/* (Python)

Note: ... represents the filepath main/java/org/dbiir/txnsails.

Analyzer:

Executor:

Adapter:

Client Libs

Interface description

We provide four apis for clients:

• register() -> (status, serverSideIdx):
• analyse() -> (status):
• execute() -> (status, results/errorMsg):
• commit()/rollback() -> (status):

Application developer should rebuild a portion of their code to utilize TxnSails' capabilities and TxnSails can automatically guarantee the serializable. Note that we do not modify the application workload to achieve serializable, for example, we do not either promote reads to writes or introduce outside lock manager. We would continue to improve above apis and TxnSails to support serializable transactions for more heterogeneous database systems, thereby further reducing application development costs.

How to use

Online workflow:

1. connect to TxnSailsSever;
2. invoke

Offline workflow:

Evaluation

Environment and Configuration

We conducted our experiments on two servers, each equipped with an Intel(R) Xeon(R) Platinum 8361HC CPU @ 2.60GHz processor, which includes 24 physical cores, 64 GB of DRAM, and a 500 GB SSD. The operating system was CentOS Linux release 7.9.

We utilize BenchBase as our benchmark simulator, deploying it on a single server. We modify it to interface with TxnSails. By default, the experiments are conducted using 128 client terminals.

We deployed PostgreSQL 15.2 as the database engine. For our database configuration, we allocated a buffer pool size of 24GB, limited the maximum number of connections to 2000, and established a lock wait timeout of 100 ms. To eliminate network-related variables from affecting the results, both TxnSails and PostgreSQL were deployed on another server.

How to Build

TxnSails requires JDK 21 and Maven 3.9+ for compilation. To run the build scripts, you need to ensure that Python 3.9+ is installed. Meanwhile, the graph learning module requires .

You can run the following command to build TxnSails server:

mvn clean package -Dmaven.javadoc.skip=true -Dcheckstyle.skip=true -Drat.skip=true -Djacoco.skip=true -DskipITs -DskipTests

This command will compile the project and the fat jar can be found in target folder.

How to Run

We provide python scripts located in the scripts/ folder to generate the corresponding .xml configuration files. Before running the tests, you should modify the information in the python script to ensure the generation of configuration files that meet the requirements, including the JDBC connection URL to connect to the database, and the database username and password.

For example, you can run the following command generate your ycsb configuration files:

python3 gen_ycsb_config.py

After you have completed the compilation and generated necessary configuration files, you can run the benchmark tests using the run_test.py script. TxnSails does not include the data loading part. That means you should load data into the database by yourselves before running the tests. The schemas for all benchmarks (SmallBank, TPC-C, YCSB) are located in /config.

You can run the following command to get help:

python3 runTxnSailsServer.py -h

The following options are provided:

  -h, --help            show this help message and exit
  -f {scalability,hotspot-128,skew-128,wc_ratio-256,bal_ratio-128,wc_ratio-128,random-128,no_ratio-128,pa_ratio-128,wr_ratio-128} [{scalability,hotspot-128,skew-128,wc_ratio-256,bal_ratio-128,wc_ratio-
128,random-128,no_ratio-128,pa_ratio-128,wr_ratio-128} ...], --function {scalability,hotspot-128,skew-128,wc_ratio-256,bal_ratio-128,wc_ratio-128,random-128,no_ratio-128,pa_ratio-128,wr_ratio-128} [{scalability,hotspot-128,skew-128,wc_ratio-256,bal_ratio-128,wc_ratio-128,random-128,no_ratio-128,pa_ratio-128,wr_ratio-128} ...]
                        specify the function
  -w {ycsb,tpcc,smallbank}, --workload {ycsb,tpcc,smallbank}
                        specify the workload
  -e {postgresql}, --engine {postgresql}
                        specify the workload
  -n CNT, --cnt CNT     count of execution
  -p PHASE, --phase PHASE
                        online predict or offline training

Running example

You can run the command to execute the hotspot-128 test of the SmallBank benchmark in PostgreSQL:

python3 runTxnSailsServer.py -w ycsb -f dynamic-128 -e postgresql -p online

python3 runTxnSailsServer.py -w smallbank -f hotspot-256 -e postgresql -p online

Note: you should replace the prefix_cmd_local, prefix_cmd_remote_java, remote_client_dir, config_prefix, and remote_machine_ip with your own configuration.