Confluo is a system for real-time monitoring and analysis of data, that supports:
- high-throughput concurrent writes of millions of data points from multiple data streams;
- online queries at millisecond timescale; and
- ad-hoc queries using minimal CPU resources.
Please find detailed documentation here.
Required dependencies:
- MacOS X or Unix-based OS; Windows is not yet supported.
- C++ compiler that supports C++11 standard (e.g., GCC 5.3 or later)
- CMake 3.2 or later
- Boost 1.58 or later
For python client, you will additionally require:
- Python 2.7 or later
- Python Packages: setuptools, six 1.7.2 or later
For java client, you will additionally require:
- Java JDK 1.7 or later
- ant 1.6.2 or later
To download and install Confluo, use the following commands:
git clone https://github.com/ucbrise/confluo.git
cd confluo
mkdir build
cd build
cmake ..
make -j && make test && make installWhile Confluo supports multiple execution modes, the simplest way to get started is to start Confluo as a server daemon and query it using one of its client APIs.
To start the server daemon, run:
confluod --address=127.0.0.1 --port=9090Here's some sample usage of the Python API:
import sys
from confluo.rpc.client import RpcClient
from confluo.rpc.storage import StorageMode
# Connect to the server
client = RpcClient("127.0.0.1", 9090)
# Create an Atomic MultiLog with given schema for a performance log
schema = """{
timestamp: ULONG,
op_latency_ms: DOUBLE,
cpu_util: DOUBLE,
mem_avail: DOUBLE,
log_msg: STRING(100)
}"""
storage_mode = StorageMode.IN_MEMORY
client.create_atomic_multilog("perf_log", schema, storage_mode)
# Add an index
client.add_index("op_latency_ms")
# Add a filter
client.add_filter("low_resources", "cpu_util>0.8 || mem_avail<0.1")
# Add an aggregate
client.add_aggregate("max_latency_ms", "low_resources", "MAX(op_latency_ms)")
# Install a trigger
client.install_trigger("high_latency_trigger", "max_latency_ms > 1000")
# Load some data
off1 = client.append([100.0, 0.5, 0.9, "INFO: Launched 1 tasks"])
off2 = client.append([500.0, 0.9, 0.05, "WARN: Server {2} down"])
off3 = client.append([1001.0, 0.9, 0.03, "WARN: Server {2, 4, 5} down"])
# Read the written data
record1 = client.read(off1)
record2 = client.read(off2)
record3 = client.read(off3)
# Query using indexes
record_stream = client.execute_filter("cpu_util>0.5 || mem_avail<0.5")
for r in record_stream:
print r
# Query using filters
record_stream = client.query_filter("low_resources", 0, sys.maxsize)
for r in record_stream:
print r
# Query an aggregate
print client.get_aggregate("max_latency_ms", 0, sys.maxsize)
# Query alerts generated by a trigger
alert_stream = client.get_alerts(0, sys.maxsize, "high_latency_trigger")
for a in alert_stream:
print aPlease create a GitHub issue to file a bug or request a feature. We welcome pull-requests, but request that you review the pull-request process before submitting one.
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