Performance Evaluation of TickTock (A new Time Series DB) - RaspberryPI Edition

Yi Lin, Feb 15, 2022

TickTock is an open source Time Series DataBase (TSDB) for DevOps, Internet of Things (IoT), and financial data. Based on many years of unsatisfied experience with TSDBs, the design goals of TickTock is set as:

  • Low resource requirement: It can run even with very low resources.
  • High performance: At least 10X better than OpenTSDB, competitive to the best TSDBs like InfluxDB. In our performance testing with some public available benchmarks, TickTock is at least 50X better than Opentsdb, 10X than InfluxDB.
  • Easy to install and maintain: Many TSDBs are built on top of other DBs, e.g., OpenTSDB on HBase, Clickhouse and Druid on relational DBs. This incurs complexity in installation and maintenance besides performance overhead. TickTock is natively developed in C++ and has a single process only. It doesn’t require additional expertise like HBase for OpenTSDB in maintence. It doesn’t have painful Garbage Collection issues to deal with in high load scenarios.
  • Replication and Scalability supports: TickTock provides replication and partition features in its open source versions.
  • Compatible with OpenTSDB APIs: OpenTSDB is one of the most widely used TSDBs. TickTock includes storing and querying APIs compatible with OpenTSDB. You can use OpenTSDB’s TCollector to collect data and use Grafana to visualize data.

Here we would like to present you a performance evaluation of TickTock. TickTock can run in X86/ARM, and 64/32 bit OS. To demonstrate the light weight characteristics of TickTock, we select a very tiny IoT Single Board Computer (SBC) device, RaspberryPI (ARMv6 32 bit), as our server.

(Note: The following is just copy & paste from TickTock wiki. You can find TickTock in github here.)

1. RaspberryPI Introduction

RaspberryPI is the most popular IoT devices. RaspberryPI is a series of tiny and affordable Single Board Computers (SBC) developed in UK, which has been sold over 40 million units since 2012. It has been used extensively in hobby and industry projects like robotics, environment monitoring etc.

  • It is tiny, as shown in the figure below.
  • It is affordable, ranged from $4 to $75.
  • It has cpu, memory, network, and disk, and runs a full OS.

The figure shows a PI-zero-W, a Single Board Computer (SBC) with

  • 1GHz single-core CPU (ARMv6),
  • 512MB memory,
  • 802.11 b/g/n wireless LAN,
  • running Raspberry PI OS (RaspBian), a Debian-based 32 bit Linux OS.
  • And it costs only $10.
RaspBerry PI-zero-wireless

PI-zero-W is the lowest and cheapest PI model with a full OS and wireless LAN. PI pico is cheaper ($4) but it is a Micro Control Unit (MCU) and not designed to run general applications. In this performance evaluation, we used PI-zero-W to show how TickTock performs in such a small IoT device.

2. IoTDB-benchmark Introduction

We select IoTDB-benchmark for performance evaluation. IoTDB-benchmark is developed by THULAB, Tsinghua University, Beijing, to compare the performance of different TSDBs with flexible test settings and IoT industrial scenarios. It was published in CoRR 2019 (Computing Research Repository). You can download the PDF paper here.

author = {Rui Liu and Jun Yuan},
title = {Benchmark Time Series Database with IoTDB-Benchmark for IoT Scenarios},
journal = {CoRR},
volume = {abs/1901.08304},
year = {2019},
url = {},
timestamp = {Sat, 02 Feb 2019 16:56:00 +0100},

IoTDB-benchmark simulates a wind power company operating several wind farms. There are many wind turbines (i.e., devices) in a wind farm, and each device has many sensors to collect different metrics such as wind speed, temperature etc periodically and send them to TSDBs.

IoTDB-benchmark is a great benchmark due to the followings:

  • It provides detailed measurement metrics such as throughput, latency (average, p10, p25, median, p75, p95, p99, p999).
  • It provides adaptors to various TSDBs such as InfluxDB, OpenTSDB, TimescaleDB. TickTock reuses the adaptor for OpenTSDB. By the way, THULAB in Tsinghua University also developed a TSDB, IoTDB which is an Apache project. So the benchmark also supports IoTDB.
  • It supports Out-Of-Order write which is a common scenario but not supported by many other TSDB benchmarks or TSDBs.
  • It supports different test scenarios, e.g, write only, read and write mixed.
  • It provides various data distributions to simulate industrial scenarios.

IoTDB-benchmark provides an opened-source implementation in github/iotdb-benchmark. We used a forked version of it in Please read the user guide.

3. Experiment settings

3.1. Hardware

We run a TickTock in a PI-zero-W, and IoTDB-benchmark in an Ubuntu laptop (1 core CPU: Intel i3–6100U@2.30GHz, 8GB memory). The laptop connects a portable mini travel wireless pocket router (GL-AR300M16) through a wired connection. The PI-zero-W connects the router with 2.4GHz wireless connection.

3.2. Software

  1. TickTock
  • Version: 0.3.8
  • Important settings: (Example config here)
  • a). Use TCP protocol for writes, and HTTP for reads;
  • b). Page count: 1024

2. IoTDB-benchmark

  • Version:
  • We test two modes:
  • a). Write-only: We aim to evaluate TickTock in the scenario of 100% writes.
  • b). Read-Write: We aim to evaluate TickTock in the scenario of mixed reads(50%) and writes(50%).
  • Important settings:
  • a). Loop: 1M (which keeps TickTock run for more than 2 hours in each test with a specific client number and device number)
  • b). Number of sensor per device: 10
  • c). We scale test loads by number of client (i.e., 1, 3, 5, 7, 9).
  • d). We bind each client to one device. So we will update CLIENT_NUMBER and DEVICE_NUMBER in for each test.

As mentioned above, we chose 1M loops in all tests. A 9-client write-only test with 1M loop will have 900M data points inserted. And it runs for more than 2 hours. We could have chosen more loops but the whole evaluation will take much longer time to finish. We did compare test results of 1M loops with 10M loops in one case, and they are very closed (with 5% difference).

For comparison purpose, we pick InfluxDB since it is the most popular TSDB and one of very few TSDBs which can run in the very tiny ARM 32 bit SBC device, PI-zero-W. If you look up TSDB in RaspberryPI forum, InfluxDB is the de facto option for TSDB.

There are many other TSDBs such as Prometheus, IotDB, TimescaleDB, Opentsdb, TDEngine etc. But most of them do not run in RPI such a small IoT ARM device with 32 bit OS. We also compared TickTock with other TSDBs in X86 64 bit environments. The experiment is still in progress and you can view the preliminary results in TickTock wiki here.

3. InfluxDB

4. Test scenario 1: Write-only mode

  • We will increase number of client and number of device in each test.
  • Example Config for 5 clients:

Note that:

  • It contains out-of-order writes (OUT_OF_ORDER_RATIO=0.5)
  • A test with 1M loops for 1 client will insert 100M data points, and 900M data points for a 9 clients test.

4.1. Throughput (DataPoint/second)

Based on our observation, the max throughput TickTock achieves is over 58K data points/second when client number is 5. The throughput of 1 client is lower since CPU is not saturated yet. The throughput of 9 clients degrades due to more concurrency.

4.2. Latency

In average, write operations were responded very fast, less than 1 millisecond. Latency increases while client number increases. We think it is due to more concurrency in processing requests.

The above figure shows the latency of 1 client test. Other client tests are similar. The percentile latencies grow as percentile grows, as expected. There was a jump from P95 to P99 latency.

4.3. CPU & Memory consumption

Based on our observation, CPUs were saturated in most cases. The 1-client (left most) still had some CPU idle throughout the test. So we can see its throughput lower than others. The 7-client case also didn’t saturate the CPU in the early stage but fully saturated in the rest of test. Its throughput is better than 1-client but worse than 3 and 5-client.

There were still plenty memories available during all tests, over 300MB (Note the total memory of PI-zero-W is 512MB). This demonstrates that TickTock is very memory efficient while CPU intensive.

5. Test scenario 2: Read-Write mixed mode

We will increase number of client and number of device in each test.

The mixed ratio:

  1. write(INGESTION): 50%
  • It contains out-of-order writes (OUT_OF_ORDER_RATIO=0.5)
  • A test with 1M loops for 1 client will insert 50M data points, and 450M data points for a 9 clients test.

2. read: 50% evenly distributed in 5 types of reads below. Other read types in IoTDB-benchmark are not supported by TickTock yet.

  • 10% PRECISE_POINT( i.e., select v1… from data where time=? and device in ?)
  • 10% TIME_RANGE(i.e., select v1… from data where time > ? and time < ? and device in ?)
  • 10% AGG_RANGE( i.e., select func(v1)… from data where device in ? and time > ? and time < ?, func() represents an aggregation function, such as avg, min, etc.)
  • 10% GROUP_BY (Group by time range query)
  • 10% LATEST_POINT( i.e., select time, v1… where device = ? and time = max(time))

5.1. Write operations (INGESTION)

Throughput and latencies of Write operations in Read-Write mode

5.1.1. Throughput (DataPoint/second)

Compared with write throughput (over 50k data points/second) in Write-only mode, the write throughput of Read-Write mixed mode is less than half of it, about 22k data points/second. Write throughput of 1 client test is lower than other tests. But the throughput does not degrade much when we increased the client number.

5.1.2. Latency

The write latency is still very fast, less than 1 millisecond in most cases, even in case of P99 latency. This is much better than the write-only mode whose P99 latency is over 100 milliseconds.

5.2. Read operation 1 (PRECISE_POINT)

Throughput and latencies of Read operation PRECISE_POINT in Read-Write mode

5.2.1. Throughput (DataPoint/second)

Read throughput was stable while we increased the client number.

5.2.2. Latency

Read latency of different percentiles grows as percentile grows, as expected. Unlike write operations, there is no big jump or cliff from percentile to next percentile.

Other read operations below have very similar pattern as PRECISION_POINT read. We won’t add explanation below for simplicity.

5.3. Read operation 2 (TIME_RANGE)

5.4. Read operation 3 (AGG_RANGE)

5.5. Read operation 4 (GROUP_BY)

5.6. Read operation 5 (LATEST_POINT)

6. Test scenario 3: Compared with InfluxDB

We compare TickTock with InfluxDB in Read-Write mixed mode. We use the case of 5 clients and 5 devices only. Other scenarios are similar so we skip the results.

6.1 Throughput comparison (the higher the better)

The test indicates that TickTock is consistently 15x better than InfluxDB in terms of throughput, in write and all reads.

6.2 Latency comparison (the lower the better)

The tests indicate that TickTock latency is much faster than InfluxDB. The read latency of TickTock is about 3.20x to 38.95x faster.

The write latency gap is even higher. However, it is worth noting that TickTock’s TCP writes are async, which means TickTock server responses after receiving write data and before applying the data. We suggest readers to read the difference between TCP and HTTP writes in our document here. For fair comparison, we should have used HTTP for writes. But we argue that IoT devices (e.g., PI-zero-W) very likely have very unstable network connections, and TCP writes should be better than HTTP writes, and be the default option. So we select TCP writes for this comparison. Actually in all tests with thousands million data points to be inserted, there was no even a single failed operation and data point.

We may provide HTTP write results in the future if we have time and resource.

6.3 CPU and Memory comparison

The above figure shows the CPU and memory consumption in testing TickTock and InfluxDB. The TickTock test only took about 2.5 hours (11556 seconds actually) to finish 1M loops (inserting about 500M data points) while the InfluxDB test took about 2 days, because of their throughput difference.

Both TickTock and InfluxDB used almost 100% of CPU. Note that TickTock uses less CPU.usr but more CPU.sys than InfluxDB. It is due to their difference in design and implementation.

TickTock used about 208MB memory (the worst memavailable is 304MB). InfluxDB used much more memory (the worst memavailable was 55MB). And you can see InfluxDB used more and more memory, until the test finished. We actually observed that the benchmark test progress was slower and slower for InfluxDB. We didn’t observe the same behavior in TickTock, even when running 10M loops (no shown in the above figure).

7. Conclusion

  1. TickTock can run in a very tiny IoT device, PI-zero-W, with ARM 32 bit OS.

2. We evaluated TickTock in both write-only mode and read-write mixed (50% to 50%) mode.

  • TickTock can achieve over 58K data points/second write throughput in write-only mode, and 22k in read-write mixed mode.
  • Read and write latency of TickTock is very fast. P95 and P99 of writes in write-only and mixed mode, respectively, can be done less than 1 millisecond.

3. We compared TickTock with InfluxDB in Read-Write mixed mode, with 5 clients.

  • TickTock is 15x better in terms of write throughput.
  • TickTock is also faster than InfluxDB. Average write latency of TickTock is 36x faster than InfluxDB. Average read latency of TickTock is from 6x to 26x faster than InfluxDB.
  • Both TickTock and InfluxDB consumed all CPUs. But TickTock consumes much less memory than InfluxDB.

Thanks for your interests and patience to read such a long post! You are more than welcome to follow TickTock at



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