Storing Cassandra Metrics (Chris Lohfink, DataStax) | C* Summit 2016

Chris Lohfink

Cassandra Metrics

© DataStax, All Rights Reserved. 2

About me

• Software developer at DataStax• OpsCenter, Metrics & Cassandra interactions


What this talk is• What does the thing the metrics report mean (da dum tis)• How metrics evolved in C*

CollectingNot how, but what and why

Cassandra Metrics

• For the most part metrics do not break backwards compatibility• Until they do (from deprecation or bugs)

• Deprecated metrics are hard to identify without looking at source code, so their disappearance may have surprising impacts even if deprecated for years.

• i.e. Cassandra 2.2 removal of “Recent Latency” metrics


C* Metrics Pre-1.1


• Classes implemented MBeans and metrics were added in place• ColumnFamilyStore -> ColumnFamilyStoreMBean

• Semi-adhoc, tightly coupled to code but had a “theme” or common abstractions

Latency Tracker

• LatencyTracker stores: • recent histogram• total histogram• number of ops• total latency

• Use latency/#ops since last time called to compute “recent” average latency

• Every time queried it will reset the latency and histogram.


https://github.com/apache/cassandra/blob/cassandra-1.0/src/java/org/apache/cassandra/utils/LatencyTracker.java


Describing Latencies

0 100 200 300 400 500 600 700 800 900 1000

• Listing the raw the values:

13ms, 14ms, 2ms, 13ms, 90ms, 734ms, 8ms, 23ms, 30ms

• Doesn’t scale well• Not easy to parse, with larger amounts can be difficult to find high values



0 100 200 300 400 500 600 700 800 900 1000

• Average:• 103ms



0 100 200 300 400 500 600 700 800 900 1000




0 100 200 300 400 500 600 700 800 900 1000


• Missing outliers



0 100 200 300 400 500 600 700 800 900 1000


• Missing outliers• Max: 734ms• Min: 2ms

Latency Tracker

• LatencyTracker stores: • recent histogram• total histogram• number of ops• total latency

• Use latency/#ops since last time called to compute “recent” average latency

• Every time queried it will reset the latency and histogram.


https://github.com/apache/cassandra/blob/cassandra-1.0/src/java/org/apache/cassandra/utils/LatencyTracker.java


Recent Average Latencies

0 100 200 300 400 500 600 700 800 900 1000

• Reported latency from• Sum of latencies since last called• Number of requests since last called


• Outliers lost

Histograms• Describes frequency of data


1, 2, 1, 1, 3, 4, 3, 1


1


1, 2, 1, 1, 3, 4, 3, 1


12


1, 2, 1, 1, 3, 4, 3, 1


112


1, 2, 1, 1, 3, 4, 3, 1


1112


1, 2, 1, 1, 3, 4, 3, 1


11123


1, 2, 1, 1, 3, 4, 3, 1


111234


1, 2, 1, 1, 3, 4, 3, 1


1112334


1, 2, 1, 1, 3, 4, 3, 1


11112334


1, 2, 1, 1, 3, 4, 3, 1


11112334


1, 2, 1, 1, 3, 4, 3, 1

4

3

2

1

0 1 2 3 4Count

Histograms• "bin" the range of values

• divide the entire range of values into a series of intervals• Count how many values fall into each interval


Histograms• "bin" the range of values—that is, divide the entire range of values

into a series of intervals—and then count how many values fall into each interval


0 100 200 300 400 500 600 700 800 900 1000

13, 14, 2, 20, 13, 90, 734, 8, 53, 23, 30

Histograms

• "bin" the range of values—that is, divide the entire range of values into a series of intervals—and then count how many values fall into each interval


13, 14, 2, 20, 13, 90, 734, 8, 53, 23, 30

Histograms



2, 8, 13, 13, 14, 20, 23, 30, 53, 90, 734

Histograms



2, 8, 13, 13, 14, 20, 23, 30, 53, 90, 734

1-10 11-100 101-10002 8 1


Histograms

Approximations

Max: 1000 (actual 734)

1-10 11-100 101-10002 8 1


Histograms

Approximations


Min: 10 (actual 2)

1-10 11-100 101-10002 8 1


Histograms

Approximations


Min: 10 (actual 2)

Average: sum / count, (10*2 + 100*8 + 1000*1) / (2+8+1) = 165 (actual 103)

1-10 11-100 101-10002 8 1


Histograms

Approximations


Min: 10 (actual 2)

Average: sum / count, (10*2 + 100*8 + 1000*1) / (2+8+1) = 165 (actual 103)

Percentiles: 11 requests, so we know 90 percent of the latencies occurred in the 11-100 bucket or lower.

90th Percentile: 100

1-10 11-100 101-10002 8 1


Histograms

Approximations


Min: 10 (actual 2)

Average: sum / count, (10*2 + 100*8 + 1000) / (2+8+1) = 165 (actual 103)

Percentiles: 11 requests, so we know 90 percent of the latencies occurred in the 11-100 bucket or lower.

90th Percentile: 100

1-10 11-100 101-10002 8 1


EstimatedHistogram

The series starts at 1 and grows by 1.2 each time

1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 17, 20, 24, 29, …12108970, 14530764, 17436917, 20924300, 25109160


LatencyTrackerHas two histograms• Recent

• Count of times a latency occurred since last time read for each bin

• Total• Count of times a latency occurred since Cassandra started for each bin


Total Histogram Deltas

If you keep track of histogram last time you read it can find delta to determine how many occurred in that interval

Last

Now

1-10 11-100 101-10002 8 1

1-10 11-100 101-10004 8 2

© DataStax, All Rights Reserved.

Total Histogram Deltas

If you keep track of histogram last time you read it can find delta to determine how many occurred in that interval

Last

Now

Delta

1-10 11-100 101-10002 8 1

1-10 11-100 101-10004 8 2

1-10 11-100 101-10002 0 1


Cassandra 1.1

• Yammer/Codahale/Dropwizard Metrics introduced • Awesome!• Not so awesome…


Reservoirs

• Maintain a sample of the data that is representative of the entire set.• Can perform operations on the limited, fixed memory set as if on entire dataset

• Vitters Algorithm R• Offers a 99.9% confidence level & 5% margin of error• Simple

• Randomly include value in reservoir, less and less likely as more values seen


Reservoirs

• Maintain a sample of the data that is representative of the entire set.• Can perform operations on the limited, fixed memory set as if on entire dataset

• Vitters Algorithm R• Offers a 99.9% confidence level & 5% margin of error * When the stream has a normal distribution

Metrics Reservoirs• Random sampling, what can it miss?

– Min– Max– Everything in 99th percentile?– The more rare, the less likely to be included

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Metrics Reservoirs• “Good enough” for basic adhoc viewing but too non-deterministic for many• Commonly resolved using replacement reservoirs (i.e. HdrHistogram)

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Metrics Reservoirs• “Good enough” for basic adhoc viewing but too non-deterministic for many• Commonly resolved using replacement reservoirs (i.e. HdrHistogram)

– org.apache.cassandra.metrics.EstimatedHistogramReservoir

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Cassandra 2.2• CASSANDRA-5657 – upgrade metrics library (and extend it)

– Replaced reservoir with EH• Also exposed raw bin counts in values operation

– Deleted deprecated metrics• Non EH latencies from LatencyTracker

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https://issues.apache.org/jira/browse/CASSANDRA-5657

Cassandra 2.2• No recency in histograms• Requires delta’ing on the total bin counts currently which is beyond

some simple tooling• CASSANDRA-11752 (fixed 2.2.8, 3.0.9, 3.8)

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Storage

Storing the data• We have data, now to store it. Approaches tend to follow:

– Store all data points• Provide aggregations either pre-computed as entered, MR, or on query

– Round Robin Database• Only store pre-computed aggregations

• Choice depends heavily on requirements

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Round Robin Database• Store state required to generate the aggregations, and only store the

aggregations– Sum & Count for Average– Current min, max– “One pass” or “online” algorithms

• Constant footprint

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Round Robin Database• Store state required to generate the aggregations, and only store the aggregations

– Sum & Count for Average– Current min, max– “One pass” or “online” algorithms

• Constant footprint

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60 300 3600Sum 0 0 0Count 0 0 0Min 0 0 0Max 0 0 0

Round Robin Database> 10ms @ 00:00

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60 300 3600Sum 10 10 10Count 1 1 1Min 10 10 10Max 10 10 10

Round Robin Database> 10ms @ 00:00> 12ms @ 00:30

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60 300 3600Sum 22 22 22Count 2 2 2Min 10 10 10Max 12 12 12

Round Robin Database> 10ms @ 00:00> 12ms @ 00:30> 14ms @ 00:59

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60 300 3600Sum 36 36 36Count 3 3 3Min 10 10 10Max 14 14 14

Round Robin Database> 10ms @ 00:00> 12ms @ 00:30> 14ms @ 00:59> 13ms @ 01:10

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60 300 3600Sum 36 36 36Count 3 3 3Min 10 10 10Max 14 14 14


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60 300 3600Sum 36 36 36Count 3 3 3Min 10 10 10Max 14 14 14

Average 12

Min 10

Max 14


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60 300 3600Sum 0 36 36Count 0 3 3Min 0 10 10Max 0 14 14


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60 300 3600Sum 13 49 49Count 1 4 4Min 13 10 10Max 13 14 14

Max is a lie• The issue with the deprecated LatencyTracker metrics is that the 1 minute interval

does not have a min/max. So we cannot compute true min/max

the rollups min/max will be the minimum and maximum average

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Histograms to the rescue (again)• The histograms of the data does not have this issue. But storage is

more complex. Some options include:– Store each bin of the histogram as a metric– Store the percentiles/min/max each as own metric– Store raw long[90] (possibly compressed)

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Histogram Storage Size• Some things to note:

– “Normal” clusters have over 100 tables.– Each table has at least two histograms we want to record

• Read latency• Write latency• Tombstones scanned• Cells scanned• Partition cell size• Partition cell count

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Histogram Storage

Because we store the extra histograms we have a 600 (minimum) with upper bounds seen to be over 24,000 histograms per minute.

• Storing 1 per bin means [54000] metrics (expensive to store, expensive to read)

• Storing raw histograms is [600] metrics• Storing min, max, 50th, 90th, 99th is [3000] metrics

– Additional problems with this• Cant compute 10th, 95th, 99.99th etc• Aggregations

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Aggregating Histograms

Averaging the percentiles

[ INSERT DISAPOINTED GIL TENE PHOTO ]


Aggregating Histograms• Consider averaging the maximumIf there is a node with a 10 second GC, but the maximum latency on your other 9 nodes is 60ms. If you report a “Max 1 second” latency, it would be misleading.

• Poor at representing hotspots affects on your applicationOne node in 10 node raspberry pi cluster gets 1000 write reqs/sec while others get 10 reqs/sec. The 1 node being under heavy stress has a 90th percentile of 10 second. The other nodes are basically sub ms and writes are taking 1ms on 90 th percentile. Would report a 1 second 90th percentile, even though 10% of our applications writes are taking >10 seconds


Aggregating Histograms

Merging histograms from different nodes more accurately can be straight forward:

Node1

Node2

Cluster

1-10 11-100 101-10002 8 1

1-10 11-100 101-10002 1 5

1-10 11-100 101-10004 9 6

Histogram Storage

Because we store the extra histograms we have a 600 (minimum) with upper bounds seen to be over 24,000 histograms per minute.

• Storing 1 per bin means [54000] metrics (expensive to store, expensive to read)

• Storing raw histograms is [600] metrics• Storing min, max, 50th, 90th, 99th is [3000] metrics

– Additional problems with this• Cant compute 10th, 95th, 99.99th etc• Aggregations

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Raw Histogram storage• Storing raw histograms 160 (default) longs is a minimum of 1.2kb

bytes per rollup and hard sell

– 760kb per minute (600 tables)– 7.7gb for the 7 day TTL we want to keep our 1 min rollups at– ~77gb with 10 nodes– ~2.3 Tb on 10 node clusters with 3k tables– Expired data isn’t immediately purged so disk space can be much worse

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Raw Histogram storage• Goal: We wanted this to be comparable to other min/max/avg metric

storage (12 bytes each)– 700mb on expected 10 node cluster– 2gb on extreme 10 node cluster

• Enter compression

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Compressing Histograms• Overhead of typical compression makes it a non-starter.

– headers (ie 10 bytes for gzip) alone nearly exceeds the length used by existing rollup storage (~12 bytes per metric)

• Instead we opt to leverage known context to reduce the size of the data along with some universal encoding.

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Compressing Histograms• Instead of storing every bin, only store the value of each bin with a value > 0

since most bin will have no data (ie, very unlikely for a read histogram to be between 1-10 microseconds which is first 10 bins)

• Write the count of offset/count pairs• Use varint for the bin count

– To reduce the value of the varint as much as possible we sort the offset/count pairs by the count and represent it as a delta sequence

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Compressing Histograms0 0 0 0 1 0 0 0 100 0 0 9999999 0 0 1 127 128 129 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

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1 byte 1 byte 1 byte 1 byte 1 byte 1 byte 1 byte 1 byte




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7


{4:1, 8:100, 11:9999999, 14:1, 15:127, 16:128 17:129}

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7


{4:1, 14:1, 8:100, 15:127, 16:128, 17:129, 11:9999999}

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7


{4:1, 14:1, 8:100, 15:127, 16:128, 17:129, 11:9999999}

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7 4 1


{4:1, 14:1, 8:100, 15:127, 16:128, 17:129, 11:9999999}

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7 4 1 14 0


{4:1, 14:1, 8:100, 15:127, 16:128, 17:129, 11:9999999}

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7 4 1 14 0 8 99


{4:1, 14:1, 8:100, 15:127, 16:128, 17:129, 11:9999999}

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7 4 1 14 0 8 99 1527


{4:1, 14:1, 8:100, 15:127, 16:128, 17:129, 11:9999999}

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7 4 1 14 0 8 99 1527 16 1 17 1


{4:1, 14:1, 8:100, 15:127, 16:128, 17:129, 11:9999999}

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7 4 1 14 0 8 99 1527 16 1 17 1 11

9999870

Compressing HistogramsReal Life** results of compression:

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Size in bytesMedian 1

75th 3

95th 15

99th 45

Max** 124

Note on HdrHistogram• Comes up every couple months• Very awesome histogram, popular replacement for Metrics reservoir.

– More powerful and general purpose than EH– Only slightly slower for all it offers

A issue comes up a bit with storage:

• Logged HdrHistograms are ~31kb each (30,000x more than our average use)• Compressed version: 1kb each• Perfect for many many people when tracking 1 or two metrics. Gets painful when

tracking hundreds or thousands

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Questions?

Storing Cassandra Metrics (Chris Lohfink, DataStax) | C* Summit 2016

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