Github user nickwallen commented on a diff in the pull request:
https://github.com/apache/metron/pull/961#discussion_r173963096
--- Diff: metron-platform/metron-enrichment/Performance.md ---
@@ -0,0 +1,522 @@
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements. See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership. The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Enrichment Performance
+
+This guide defines a set of benchmarks used to measure the performance of
the Enrichment topology. The guide also provides detailed steps on how to
execute those benchmarks along with advice for tuning the Enrichment topology.
+
+* [Benchmarks](#benchmarks)
+* [Benchmark Execution](#benchmark-execution)
+* [Performance Tuning](#performance-tuning)
+* [Benchmark Results](#benchmark-results)
+
+## Benchmarks
+
+* [Geo IP Enrichment](#geo-ip-enrichment)
+* [HBase Enrichment](#hbase-enrichment)
+* [Stellar Enrichment](#stellar-enrichment)
+
+### Geo IP Enrichment
+
+This benchmark measures the performance of executing a Geo IP enrichment.
Given a valid IP address the enrichment will append detailed location
information for that IP. The location information is sourced from an external
Geo IP data source like [Maxmind](https://github.com/maxmind/GeoIP2-java).
+
+#### Configuration
+
+Adding the following Stellar expression to the Enrichment topology
configuration will define a Geo IP enrichment.
+```
+geo := GEO_GET(ip_dst_addr)
+```
+
+After the enrichment process completes, the telemetry message will
contain a set of fields with location information for the given IP address.
+```
+{
+ "ip_dst_addr":"151.101.129.140",
+ ...
+ "geo.city":"San Francisco",
+ "geo.country":"US",
+ "geo.dmaCode":"807",
+ "geo.latitude":"37.7697",
+ "geo.location_point":"37.7697,-122.3933",
+ "geo.locID":"5391959",
+ "geo.longitude":"-122.3933",
+ "geo.postalCode":"94107",
+ }
+```
+
+### HBase Enrichment
+
+This benchmark measures the performance of executing an enrichment that
retrieves data from an external HBase table. This type of enrichment is useful
for enriching telemetry from an Asset Database or other source of relatively
static data.
+
+#### Configuration
+
+Adding the following Stellar expression to the Enrichment topology
configuration will define an Hbase enrichment. This looks up the 'ip_dst_addr'
within an HBase table 'top-1m' and returns a hostname.
+```
+top1m := ENRICHMENT_GET('top-1m', ip_dst_addr, 'top-1m', 't')
+```
+
+After the telemetry has been enriched, it will contain the host and IP
elements that were retrieved from the HBase table.
+```
+{
+ "ip_dst_addr":"151.101.2.166",
+ ...
+ "top1m.host":"earther.com",
+ "top1m.ip":"151.101.2.166"
+}
+```
+
+### Stellar Enrichment
+
+This benchmark measures the performance of executing a basic Stellar
expression. In this benchmark, the enrichment is purely a computational task
that has no dependence on an external system like a database.
+
+#### Configuration
+
+Adding the following Stellar expression to the Enrichment topology
configuration will define a basic Stellar enrichment. The following returns
true if the IP is in the given subnet and false otherwise.
+```
+local := IN_SUBNET(ip_dst_addr, '192.168.0.0/24')
+```
+
+After the telemetry has been enriched, it will contain a field with a
boolean value indicating whether the IP was within the given subnet.
+```
+{
+ "ip_dst_addr":"151.101.2.166",
+ ...
+ "local":false
+}
+```
+
+## Benchmark Execution
+
+* [Prepare Enrichment Data](#prepare-enrichment-data)
+* [Load HBase with Enrichment Data](#load-hbase-with-enrichment-data)
+* [Configure the Enrichments](#configure-the-enrichments)
+* [Create Input Telemetry](#create-input-telemetry)
+* [Cluster Setup](#cluster-setup)
+* [Monitoring](#monitoring)
+
+### Prepare Enrichment Data
+
+The Alexa Top 1 Million was used as an data source for these benchmarks.
+
+1. Download the [Alexa Top 1
Million](http://s3.amazonaws.com/alexa-static/top-1m.csv.zip).
+
+2. For each hostname, query DNS to retrieve an associated IP address.
+
+ A script like the following can be used for this. There is no need to
do this for all 1 million entries in the data set. Doing this for around 10,000
records is sufficient.
+
+ ```python
+ import dns.resolver
+ import csv
+
+ resolver = dns.resolver.Resolver()
+ resolver.nameservers = ['8.8.8.8', '8.8.4.4']
+
+ with open('top-1m.csv', 'r') as infile:
+ with open('top-1m-with-ip.csv', 'w') as outfile:
+
+ reader = csv.reader(infile, delimiter=',')
+ writer = csv.writer(outfile, delimiter=',')
+ for row in reader:
+
+ host = row[1]
+ try:
+ response = resolver.query(host, "A")
+ for record in response:
+ ip = record
+ writer.writerow([host, ip])
+ print "host={}, ip={}".format(host, ip)
+
+ except:
+ pass
+ ```
+
+3. The resulting data set contains an IP to hostname mapping.
+ ```bash
+ $ head top-1m-with-ip.csv
+ google.com,172.217.9.46
+ youtube.com,172.217.4.78
+ facebook.com,157.240.18.35
+ baidu.com,220.181.57.216
+ baidu.com,111.13.101.208
+ baidu.com,123.125.114.144
+ wikipedia.org,208.80.154.224
+ yahoo.com,98.139.180.180
+ yahoo.com,206.190.39.42
+ reddit.com,151.101.1.140
+ ```
+
+### Load HBase with Enrichment Data
+
+1. Create an HBase table for this data.
+
+ Ensure that the table is evenly distributed across the HBase nodes.
This can be done by pre-splitting the table or splitting the data after loading
it.
+
+ ```
+ create 'top-1m', 't', {SPLITS => ['2','4','6','8','a','c','e']}
+ ```
+
+1. Create a configuration file called `extractor.json`. This defines how
the data will be loaded into the HBase table.
+
+ ```bash
+ > cat extractor.json
+ {
+ "config": {
+ "columns": {
+ "host" : 0,
+ "ip": 1
+ },
+ "indicator_column": "ip",
+ "type": "top-1m",
+ "separator": ","
+ },
+ "extractor": "CSV"
+ }
+ ```
+
+1. Use the `flatfile_loader.sh` to load the data into the HBase table.
+ ```
+ $METRON_HOME/bin/flatfile_loader.sh \
+ -e extractor.json \
+ -t top-1m \
+ -c t \
+ -i top-1m-with-ip.csv
+ ```
+
+### Configure the Enrichments
+
+1. Define the Enrichments using the REPL.
+
+ ```
+ > $METRON_HOME/bin/stellar -z $ZOOKEEPER
+ Stellar, Go!
+
+ [Stellar]>>> conf
+ {
+ "enrichment": {
+ "fieldMap": {
+ "stellar" : {
+ "config" : {
+ "geo" : "GEO_GET(ip_dst_addr)",
+ "top1m" : "ENRICHMENT_GET('top-1m', ip_dst_addr, 'top-1m',
't')",
+ "local" : "IN_SUBNET(ip_dst_addr, '192.168.0.0/24')"
+ }
+ }
+ },
+ "fieldToTypeMap": {
+ }
+ },
+ "threatIntel": {
+ }
+ }
+ [Stellar]>>> CONFIG_PUT("ENRICHMENT", conf, "asa")
+ ```
+
+### Create Input Telemetry
+
+1. Create a template file that defines what your input telemetry will
look-like.
+
+ ```bash
+ > cat asa.template
+ {"ciscotag": "ASA-1-123123", "source.type": "asa", "ip_dst_addr":
"$DST_ADDR", "original_string": "<134>Feb 22 17:04:43 AHOSTNAME %ASA-1-123123:
Built inbound ICMP connection for faddr 192.168.11.8/50244 gaddr
192.168.1.236/0 laddr 192.168.1.1/161", "ip_src_addr": "192.168.1.35",
"syslog_facility": "local1", "action": "built", "syslog_host": "AHOSTNAME",
"timestamp": "$METRON_TS", "protocol": "icmp", "guid": "$METRON_GUID",
"syslog_severity": "info"}
+ ```
+
+2. Use the template file along with the enrichment data to create input
telemetry with varying IP addresses.
+
+ ```bash
+ for i in $(head top-1m-with-ip.csv | awk -F, '{print $2}');do
+ cat asa.template | sed "s/\$DST_ADDR/$i/";
+ done > asa.input.template
+ ```
+
+3. Use the `load_test.sh` script to push messages onto the input topic
`enrichments` and monitor the output topic `indexing`.
+
+ If the topology is keeping up, obviously the events per second produced
on the input topic should roughly match the output topic.
+
+ ```
+ $METRON_HOME/bin/load_test.sh \
+ -e 200000 \
+ -ot enrichments \
+ -mt indexing \
+ -p 10 \
+ -t asa.input.template \
+ -z $ZOOKEEPER
+ ```
+
+ [TODO] Link to the docs that get created for the `load_test.sh` script.
+
+### Cluster Setup
+
+#### Isolation
+
+The Enrichment topology depends on an environment with at least two and
often three components that work together; Storm, Kafka, and HBase. When any
of two of these are run on the same node, it can be difficult to identify which
of them is becoming a bottleneck. This can cause poor and highly volatile
performance as each steals resources from the other.
+
+It is highly recommended that each of these systems be fully isolated from
the others. Storm should be run on nodes that are completely isolated from
Kafka and HBase.
+
+### Monitoring
+
+1. The `load_test.sh` script will report the throughput for the input and
output topics.
+
+ * The input throughput should roughly match the output throughput if
the topology is able to handle a given load.
+
+ * Not only are the raw throughput numbers important, but also the
consistency of what is reported over time. If the reported throughput is
sporadic, then further tuning may be required.
+
+1. The Storm UI is obviously an important source of information. The bolt
capacity, complete latency, and any reported errors are all important to monitor
+
+1. The load reported by the OS is also an important metric to monitor.
+
+ * The load metric should be monitored to ensure that each node is being
pushed sufficiently, but not too much.
+
+ * The load should be evenly distributed across each node. If the load
is uneven, this may indicate a problem.
+
+ A simple script like the following is sufficient for the task.
+
+ ```
+ for host in $(cat cluster.txt); do
+ echo $host;
+ ssh root@$host 'uptime';
+ done
+ ```
+
+1. Monitoring the Kafka offset lags indicates how far behind a consumer
may be. This can be very useful to determine if the topology is keeping up.
+
+ ```
+ ${KAFKA_HOME}/bin/kafka-consumer-groups.sh \
+ --command-config=/tmp/consumergroup.config \
+ --describe \
+ --group enrichments \
+ --bootstrap-server $BROKERLIST \
+ --new-consumer
+ ```
+
+1. A tool like [Kafka Manager](https://github.com/yahoo/kafka-manager) is
also very useful for monitoring the input and output topics during test
execution.
+
+## Performance Tuning
+
+The approach to tuning the topology will look something like the
following. More detailed tuning information is available next to each named
parameter
+
+* Start the tuning process with a single worker. After tuning the
individual bolts within a single worker, then scale out with more worker
processes.
+
+* Start the thread pool with a size of 1. Increase slowly only after
tuning the other parameters first. Consider that each worker has its own
thread pool and the total size of this thread pool should be less than the
total number of cores available in the cluster.
+
+* Set each parallelism value to the number of partitions on the input
Kafka topic. Use the same value for all until a bolt capacity greater than 1
shows that the parallelism for a particular bolt needs to be increased.
+
+* Parallelism units can be used for determining how to distribute
processing tasks across the topology. The sum of parallelism can be close to,
but should not far exceed this value.
+
+ (number of worker nodes in cluster * number cores per worker node) -
(number of acker tasks)
+
+* If the topology is not able to keep-up with a given input, then
increasing the parallelism should allow it to scale up.
+
+* The throughput that the topology is able to sustain should be relatively
consistent. If the throughput fluctuates greatly, increase back pressure using
[`topology.max.spout.pending`](#topology-max-spout-pending).
+
+### Parameters
+
+The following parameters are useful for tuning the "Unified" Enrichment
topology.
+
+* [`enrichment.workers`](#enrichmentworkers)
+* [`enrichment.acker.executors`](#enrichmentackerexecutors)
+* [`topology.worker.childopts`](#topologyworkerchildopts)
+* [`topology.max.spout.pending`](#topologymaxspoutpending)
+* [`kafka.spout.parallelism`](#kafkaspoutparallelism)
+* [`enrichment.join.parallelism`](#enrichmentjoinparallelism)
+* [`threat.intel.join.parallelism`](#threatinteljoinparallelism)
+* [`kafka.writer.parallelism`](#kafkawriterparallelism)
+* [`enrichment.join.cache.size`](#enrichmentjoincachesize)
+* [`threat.intel.join.cache.size`](#threatinteljoincachesize)
+* [`metron.threadpool.size`](#metronthreadpoolsize)
+* [`metron.threadpool.type`](#metronthreadpooltype)
+
+
+WARNING: Some of the parameter names have been reused from the
"Split/Join" topology so the name may not be appropriate. This will be
corrected in the future.
+
+#### `enrichment.workers`
+
+The number of worker processes for the enrichment topology.
+
+* Start by tuning only a single worker. Maximize throughput for that
worker, then increase the number of workers.
+
+* The throughput should scale relatively linearly as workers are added.
This reaches a limit as the number of workers running on a single node saturate
the resources available. When this happens, adding workers, but on additional
nodes should allow further scaling.
+
+* Increase parallelism before attempting to increase the number of workers.
+
+#### `enrichment.acker.executors`
+
+The number of ackers within the topology.
+
+* This should most often be equal to the number of workers defined in
`enrichment.workers`.
+
+* Within the Storm UI, click the "Show System Stats" button. This will
display a bolt named `__acker`. If the capacity of this bolt is too high, then
increase the number of ackers.
+
+#### `topology.worker.childopts`
+
+This parameter accepts arguments that will be passed to the Storm worker
processes. This allows for control over the heap size, garbage collection, and
any other JVM-specific parameter.
+
+* Start with a 2G heap and increase as needed. Running with 8G was found
to be beneficial, but will vary depending on caching needs.
+
+* The Garbage First Garbage Collector (G1GC) is recommended.
+
+* The following settings were found to be beneficial.
+ `-XX:+UseG1GC -Xms8g -Xmx8g -XX:MaxGCPauseMillis=100`
+
+#### `topology.max.spout.pending`
+
+This limits the number of unacked tuples that the spout can introduce into
the topology.
+
+* Decreasing this value will increase back pressure and allow the topology
to consume messages at a pace that is maintainable.
+
+* If the spout throws 'Commit Failed Exceptions' then the topology is not
keeping up. Decreasing this value is one way to ensure that messages can be
processed before they time out.
+
+* If the topology's throughput is unsteady and inconsistent, decrease this
value. This should help the topology consume messages at a maintainable pace.
+
+* If the bolt capacity is low, the topology can handle additional load.
Increase this value so that more tuples are introduced into the topology which
should increase the bolt capacity.
+
+#### `kafka.spout.parallelism`
+
+The parallelism of the Kafka spout within the topology. Defines the
maximum number of executors for each worker dedicated to running the spout.
+
+* The spout parallelism should most often be set to the number of
partitions of the input Kafka topic.
+
+* If the enrichment bolt capacity is low, increasing the parallelism of
the spout can introduce additional load on the topology.
+
+#### `enrichment.join.parallelism`
+
+The parallelism hint for the enrichment bolt. Defines the maximum number
of executors within each worker dedicated to running the enrichment bolt.
+
+WARNING: The property name does not match its current usage in the Unified
topology. This property name may change in the near future as it has been
reused from the Split-Join topology.
+
+* If the capacity of the enrichment bolt is high, increasing the
parallelism will introduce additional executors to bring the bolt capacity down.
+
+* If the throughput of the topology is too low, increase this value. This
allows additional tuples to be enriched in parallel.
+
+* Increasing parallelism on the enrichment bolt will at some point put
pressure on the downstream threat intel and output bolts. As this value is
increased, monitor the capacity of the downstream bolts to ensure that they do
not become a bottleneck.
+
+#### `threat.intel.join.parallelism`
+
+The parallelism hint for the threat intel bolt. Defines the maximum
number of executors within each worker dedicated to running the threat intel
bolt.
+
+WARNING: The property name does not match its current usage in the Unified
topology. This property name may change in the near future as it has been
reused from the Split-Join topology.
+
+* If the capacity of the threat intel bolt is high, increasing the
parallelism will introduce additional executors to bring the bolt capacity down.
+
+* If the throughput of the topology is too low, increase this value. This
allows additional tuples to be enriched in parallel.
+
+* Increasing parallelism on this bolt will at some point put pressure on
the downstream output bolt. As this value is increased, monitor the capacity
of the output bolt to ensure that it does not become a bottleneck.
+
+#### `kafka.writer.parallelism`
+
+The parallelism hint for the output bolt which writes to the output Kafka
topic. Defines the maximum number of executors within each worker dedicated to
running the output bolt.
+
+* If the capacity of the output bolt is high, increasing the parallelism
will introduce additional executors to bring the bolt capacity down.
+
+#### `enrichment.join.cache.size`
+
+The Enrichment bolt maintains a cache so that if the same enrichment
occurs repetitively, the value can be retrieved from the cache instead of it
being recomputed.
+
+There is a great deal of repetition in network telemetry, which leads to a
great deal of repetition for the enrichments that operate on that telemetry.
Having a highly performant cache is one of the most critical factors driving
performance.
+
+WARNING: The property name does not match its current usage in the Unified
topology. This property name may change in the near future as it has been
reused from the Split-Join topology.
+
+* Increase the size of the cache to improve the rate of cache hits.
+
+* Increasing the size of the cache may require that you increase the
worker heap size using `topology.worker.childopts'.
+
+#### `threat.intel.join.cache.size`
+
+The Threat Intel bolt maintains a cache so that if the same enrichment
occurs repetitively, the value can be retrieved from the cache instead of it
being recomputed.
+
+There is a great deal of repetition in network telemetry, which leads to a
great deal of repetition for the enrichments that operate on that telemetry.
Having a highly performant cache is one of the most critical factors driving
performance.
+
+WARNING: The property name does not match its current usage in the Unified
topology. This property name may change in the near future as it has been
reused from the Split-Join topology.
+
+* Increase the size of the cache to improve the rate of cache hits.
+
+* Increasing the size of the cache may require that you increase the
worker heap size using `topology.worker.childopts'.
+
+#### `metron.threadpool.size`
+
+The enrichment bolt maintains a static thread pool that is used to execute
each enrichment. This thread pool is shared by all of the executors running
within the same worker.
+
+Defines the number of threads within the thread pool used to execute each
enrichment. This value can either be a fixed number or it can be a multiple of
the number of cores (5C = 5 times the number of cores).
+
+Currently, this value must be manually defined within the flux file at
`$METRON_HOME/flux/enrichment/remote-unified.yaml`. This value cannot be
altered within Ambari.
+
+* Start with a thread pool size of 1. Adjust this value after tuning all
other parameters first. Only increase this value if testing shows performance
improvements in your environment given your workload.
+
+* If the thread pool size is too large this will cause the work to be
shuffled amongst multiple CPU cores, which significantly decreases performance.
Using a smaller thread pool helps pin work to a single core.
+
+* If the thread pool size is too small this can negatively impact
IO-intensive workloads. Increasing the thread pool size, helps when using
IO-intensive workloads with a significant cache miss rate. A thread pool size
of 3-5 can help in these cases.
+
+* Most workloads will make significant use of the cache and so 1-2 threads
will most likely be optimal.
+
+* The bolt uses a static thread pool. To scale out, but keep the work
mostly pinned to a CPU core, add more Storm workers while keeping the thread
pool size low.
+
+* If a larger thread pool increases load on the system, but decreases the
throughput, then it is likely that the system is thrashing. In this case the
thread pool size should be decreased.
+
+#### `metron.threadpool.type`
+
+The enrichment bolt maintains a static thread pool that is used to execute
each enrichment. This thread pool is shared by all of the executors running
within the same worker.
+
+Defines the type of thread pool used. This value can be either "FIXED" or
"WORK_STEALING".
+
+Currently, this value must be manually defined within the flux file at
`$METRON_HOME/flux/enrichment/remote-unified.yaml`. This value cannot be
altered within Ambari.
+
+### Benchmark Results
+
+This section describes one execution of these benchmarks to help provide
an understanding of what reasonably tuned parameters might look-like.
+
+These parameters and the throughput reported are highly dependent on the
workload and resources available. The throughput is what was achievable given a
reasonable amount of tuning on a small, dedicated cluster. The throughput is
largely dependent on the enrichments performed and the distribution of data
within the incoming telemetry.
+
+The Enrichment topology has been show to scale relatively linearly.
Adding more resources allows for more complex enrichments, across more diverse
data sets, at higher volumes. The throughput that one might see in production
largely depends on how much hardware can be committed to the task.
+
+#### Environment
+
+* Apache Metron 0.4.3 (pre-release) March, 2018
+ * This included [a patch to the underlying caching
mechanism](https://github.com/apache/metron/pull/947) that greatly improves
performance.
+
+* Cisco UCS nodes
+ * 32 core, 64-bit CPU (Intel(R) Xeon(R) CPU E5-2630 v3 @ 2.40GHz)
+ * 256 GB RAM
+ * x2 10G NIC bonded
+ * x4 6TB 7200 RPM disks
+
+* Storm Supervisors are isolated and running on a dedicated set of 3 nodes.
+
+* Kafka Brokers are isolated and running on a separate, dedicated set of 3
nodes.
+
+#### Results
+
+* These benchmarks executed all 3 enrichments simultaneously; the [Geo IP
Enrichment](#geo-ip-enrichment), [Stellar Enrichment](#stellar-enrichment) and
the [HBase Enrichment](#hbase-enrichment).
+
+* The data used to drive the benchmark includes 10,000 unique IP
addresses. The telemetry was populated with IP addresses such that 10% of
these IPs were chosen 80% of the time. This bias was designed to mimic the
typical distribution seen in real-world telemetry.
+
+* The Unified Enrichment topology was able to sustain 308,000 events per
second on a small, dedicated 3 node cluster.
+
+* The values used to achieve these results with the Unified Enrichment
topology follows.
+ ```
+ enrichment.workers=9
+ enrichment.acker.executors=9
+ enrichment.join.cache.size=100000
+ kafka.spout.parallelism=27
+ enrichment.join.parallelism=54
+ threat.intel.join.parallelism=9
+ kafka.writer.parallelism=27
+ topology.worker.childopts=-XX:+UseG1GC -Xms8g -Xmx8g
-XX:MaxGCPauseMillis=100
--- End diff --
Makes sense.. I addressed this in the latest commit. Let me know if that
works for you.
---
